A scalable business depends heavily on the kind of systems you have implemented. Businesses that still rely on manual entry of inventory items in spreadsheet documents cannot have an accurate measure of the product they have on hand. So, what’s the solution? It’s quite simple actually, your data needs to harness the power of Artificial Intelligence (AI), Machine Learning (ML), and data science.


A smart warehouse combines various interconnected technologies to form an ecosystem whereby an entire business operation, from supply to delivery, is governed by AI. Goods are received at the warehouse, identified and sorted, processed, packaged, and pulled for shipment, all automatically and with minimal margin for error. Warehouse management will become more agile, with faster responses to logistical demands of material items and personnel, and more scalable in terms of finding new solutions for greater volume and flow of product.


Fully automated warehousing has not yet been achieved, but these are some of the ways AI will transform warehouse management:


Computer vision and AI can be used to track the inventory within the warehouse. Cameras can be installed at various points in the warehouse to monitor the location of goods. AI algorithms can then be used to analyze the video footage and identify the location of each item, making it easy to track and manage inventory levels. RFID (radio frequency identification) replaces paper trails and barcode scanners for the organization and control of inventory, tracking products with digital tags and enabling a more precise and accurate inventory control.

In terms of inspection, computer vision is a game-changer for logistics companies. Instead of verifying each parcel manually, they can let the machines do the job and focus on more demanding, higher-value tasks. Trained with depictions of flawless products, the AI models quickly detect any defect or anomaly. Based on the output, the computer vision-powered system makes an autonomous decision to redirect the item for manual inspection. Such a solution offloads the employees while helping the company maintain the highest customer satisfaction rates.


Computer vision and AI can be used to streamline the order-picking process. Cameras can be installed to capture images of the products, which can then be analyzed by AI algorithms to identify the items that need to be picked for each order. This can help to reduce the time it takes to fulfill orders and improve the accuracy of the picking process. Some machines can now even pack the products themselves, using AI to optimize the space and materials.

AI is already transforming warehouses with the use of robots, which can pick up goods and redistribute them within a fraction of the time required by human operatives. Computer vision can be used to enable autonomous vehicles to move around the warehouse. Cameras and sensors can be used to help vehicles navigate around obstacles and find the most efficient route to their destination. This can help to reduce the time it takes to move goods around the warehouse and improve the overall efficiency of the operation.

AI can be used to analyze data from sensors and cameras within the warehouse to predict when equipment is likely to fail. This can help to prevent downtime and reduce the costs associated with maintenance and repair. machine-learning algorithms enable detailed stock movement forecasting and management to fine-tune material handling. In this way, operator error and processing times can be reduced, with corresponding increases in overall efficiency and productivity.

Data Annotation for AI-based Warehouse Management


Warehouse and inventory management are critical components of supply chain management that involve the systematic storage and tracking of goods and products. AI-based solutions can significantly improve the efficiency and accuracy of warehouse and inventory management, leading to better customer service, reduced costs, and increased profits.

To train an AI-based model for warehouse and inventory management, data is collected from various sources, including historical inventory data, sales data, and shipping data. The data is then preprocessed, cleaned, and organized to remove any inconsistencies, errors, or duplicates. This is where our data annotation services come in, as they can help in ensuring the accuracy and completeness of the data.

Once the data is preprocessed and cleaned, it is split into training, validation, and testing sets. The training set is used to train the AI-based model, while the validation and testing sets are used to evaluate the performance of the model.

The AI-based model is then developed using a combination of supervised and unsupervised learning techniques. For supervised learning, annotated data is used, which is labeled with the correct output. Our data annotation services can provide the required labeled data for the supervised learning process. For unsupervised learning, the model is trained on unlabeled data, and the algorithm learns to recognize patterns and relationships in the data.


After the model is trained, it is tested to ensure it performs accurately and consistently. The model is then deployed into the warehouse and inventory management system, where it can be used to optimize inventory levels, reduce out-of-stock situations, improve delivery times, and minimize excess inventory.

TagX can play a critical role in preparing the data required to train AI-based models for warehouse and inventory management. With accurate and complete data, AI-based models can significantly improve the efficiency and effectiveness of warehouse and inventory management, leading to better business outcomes.

AI is, without a doubt, a revolutionary technology. If AI is not currently used in your warehouse, it may be time to start. Here are some immediate and long-term ways artificial intelligence can enhance your business operations:


Although there will always be a need for human labor, there are some tasks that warehouse automation robots can perform more quickly and accurately. AI-powered systems can optimize tasks and streamline operations, leading to improved efficiency and faster turnaround times. For example, automated picking systems can increase picking speed and accuracy, reducing the time and labor required for manual processes.


AI tools can continuously monitor the environment and activities in a warehouse. They can evaluate activities and assign them a risk score. High-risk tasks can be delegated to robots, leaving humans to handle safer duties. For instance, autonomous mobile robots can navigate through the warehouse floor and avoid obstacles, reducing the need for human intervention and minimizing the risk of collisions.


Adopting AI solutions can reduce costs for your company by fostering efficiencies and reducing warehouse overhead. These systems can help warehouses reduce costs by optimizing workflows and minimizing waste. For example, machine learning algorithms can predict demand patterns and optimize inventory levels, reducing the cost of holding excess inventory.


AI can assist your warehouse in determining market conditions, allowing you to manage your inventory better. AI solutions can simplify inventory management by providing real-time visibility into inventory levels, locations, and movements. This helps warehouse managers optimize inventory levels and reduce stock-outs, while also minimizing the risk of overstocking.


AI-powered warehouses can improve the overall customer experience by enabling faster delivery times and more accurate order fulfillment. This helps businesses differentiate themselves from competitors and improve customer satisfaction. Companies can better understand their current situation, spot patterns, and develop strategic plans with the algorithmic analysis provided by AI.




Using automated inventory management can aid in both physical tasks such as the relocating and tracking of items to more complex tasks where data and advanced insights are required for error-free planning and demand forecasts. It saves on costs, and manpower, and can bring added security by monitoring systems to notify you of any potential ransomware or cyber-attacks.


As businesses grow, AI-powered methods will become necessary to improve inventory and secure long-term growth. Along with a powerful cloud to secure your data and spin up workloads quickly, using AI will be essential to the success of your business. Get ahead of the game now by deploying artificial intelligence inventory management today with 

How to leverage AI and Data Annotation for Warehouse management

Humans can easily detect and identify objects present in an image. The human visual system is fast and accurate and can perform complex tasks like identifying multiple objects and detecting obstacles with little conscious thought. With the availability of large amounts of data, faster GPUs, and better algorithms, we can now easily train computers to detect and classify multiple objects within an image with high accuracy.

With this kind of identification and localization, object detection can be used to count objects in a scene and determine and track their precise locations, all while accurately labeling them.

Object detection is a key field in artificial intelligence, allowing computer systems to “see” their environments by detecting objects in visual images or videos. Object detection is often called object recognition, object identification, and image detection, and these concepts are synonymous.

Object detection is an important computer vision task used to detect instances of visual objects of certain classes (for example, humans, animals, cars, or buildings) in digital images such as photos or video frames. The goal of object detection is to develop computational models that provide the most fundamental information needed by computer vision applications: “What objects are where?”.

Object detection is not, however, akin to other common computer vision technologies such as classification (assigns a single class to an image), keypoint detection (identifies points of interest in an image), or semantic segmentation (separates the image into regions via masks).

As with every emerging tech, there are plenty of technical terms that might cause confusion or be thought of as synonyms when it comes to computer vision. There’s classification, detection, tracking, counting, and more. However, one of the biggest confusion points involves object detection and image classification. At the most basic level, the difference between classification and detection is simple:

Image Classification: Applies a prediction to an image based on an analysis of the contents.

Objection Detection: Locates objects within an image.

Object detection is one of the fundamental problems of computer vision. It forms the basis of many other downstream computer vision tasks, for example, instance segmentation, image captioning, object tracking, and more. Specific object detection applications include pedestrian detection, people counting, face detection, text detection, pose detection, and number-plate recognition.

How Are Object Recognition Models Trained?

The AI model training process for Object recognition is similar to that of Image recognition. However, there’s one crucial difference: the labels for the input dataset.

Object recognition datasets bundle together an image or video with a list of objects it contains and their locations.

Before training an Object recognition model, machine learning experts need to decide which categories they would like the AI model to recognize. For example, a simple Mask detection model might classify faces in images as “with mask,” ” or “without a mask.” Each faces in the image or video in the training dataset needs to be associated with one of these labels so that the model can learn it during the training process.

Once the Object recognition model is trained, it can start analyzing real-world data. The model accepts an image as input and returns a list of predictions for the image’s label. The more data you give your model, the better your device will be at recognizing the objects you want and learning how to improve for the future.

Object Detection Use Cases and Applications

The use cases involving object detection are very diverse; there are almost unlimited ways to make computers see like humans to automate manual tasks or create new, AI-powered products and services. It has been implemented in computer vision programs used for a range of applications, from sports production to productivity analytics. Today, object recognition is the core of most vision-based AI software and programs. Object detection plays an important role in scene understanding, which is popular in security, transportation, medical, and military use cases.

Object detection in Retail. Strategically placed people counting systems throughout multiple retail stores are used to gather information about how customers spend their time and customer footfall. AI-based customer analysis to detect and track customers with cameras helps to gain an understanding of customer interaction and customer experience, optimize the store layout, and make operations more efficient. A popular use case is the detection of queues to reduce waiting time in retail stores.

Autonomous Driving. Self-driving cars depend on object detection to recognize pedestrians, traffic signs, other vehicles, and more. For example, Tesla’s Autopilot AI heavily utilizes object detection to perceive environmental and surrounding threats such as oncoming vehicles or obstacles.

Video surveillance -Because state-of-the-art object detection techniques can accurately identify and track multiple instances of a given object in a scene, these techniques naturally lend themselves to automated video surveillance systems. For instance, object detection models are capable of tracking multiple people at once, in real-time, as they move through a given scene or across video frames. From retail stores to industrial factory floors, this kind of granular tracking could provide invaluable insights into security, worker performance and safety, retail foot traffic, and more. Example of object detection in video analytics for people detection in dangerous areas using CCTV cameras

Vehicle detection with AI in Transportation. Object recognition is used to detect and count vehicles for traffic analysis or to detect cars that stop in dangerous areas, for example, on crossroads or highways.

Animal detection in Agriculture. Object detection is used in agriculture for tasks such as counting, animal monitoring, and evaluation of the quality of agricultural products. Damaged produce can be detected while it is in processing using machine learning algorithms.

Medical feature detection in Healthcare. Object detection has allowed for many breakthroughs in the medical community. Because medical diagnostics rely heavily on the study of images, scans, and photographs, object detection involving CT and MRI scans has become extremely useful for diagnosing diseases, for example with ML algorithms for tumor detection.

Making object recognition becomes possible with data labeling service. Human annotators spent time and effort manually annotating each image producing a huge quantity of datasets. Machine learning algorithms need the bulk of the huge amount of training data to make train the model.

In data annotation, thousands of images are annotated using various image annotation techniques assigning a specific class to each image. Usually, most AI companies don’t spend their workforce or deploy such resources to generate the labeled training datasets.

TagX is the industry leader in providing high-quality training datasets for machine learning and deep learning. Working with renowned clients, it is offering data annotation for computer vision and NLP-based AI model developments.

Introduction to Object Detection for Computer Vision and AI

Can you imagine an industry that involves more challenge than agriculture? You reap what you sow, they say. But what they forget to add is “if you’re lucky.” When the weather strikes or crops get affected by disease, farmers can hardly talk about yields. Or when a global pandemic hits, all of a sudden it gets harder to manage various processes because most are not digital.

At the same time, the global population is growing, and urbanization is continuing. Disposable income is rising, and consumption habits are changing. Farmers are under a lot of pressure to meet the increasing demand, and they need a way to increase productivity. Thirty years from now, there will be more people to feed. And since the amount of fertile soil is limited, there will also be a need to move beyond traditional farming.

We need to look for ways to help farmers minimize their risks, or at least make them more manageable. Implementing artificial intelligence in agriculture on a global scale is one of the most promising opportunities.

Artificial Intelligence is being used by the agriculture industry to help produce healthier crops, control pests, monitor soil and growing conditions, organise data for farmers, reduce effort, and improve a wide range of agriculture-related operations along the food supply chain.

In agriculture field image annotation helps to make crops and other things recognizable to make the right decision without use of humans. So, let’s find out what image annotation can do for an agricultural field and how it is utilized in machine learning and AI.

The robots used agriculture and farming to detect the crops including fruits and vegetables for performing various tasks. Image annotation annotates the crops to make them recognizable to machine learning models like robots or drones.

Annotation to Check Plants Fructification

Just like detecting the crops, image annotation also helps to check the fructification level of plants, if they are ready for harvesting or what is the maturity level of plants. Image annotation techniques can help to detect such plants and inform the farmers to take actions as per the crops Fructification levels.

Apart from detecting the crops, image annotation also helps computer vision models to check or monitor the health of the crops through deep learning AI model training. The robots can closely monitor the crop or plant and analyze its condition whether its matured, not matured, infected or need pesticides to protect from insects and other harmful pests.

Animal husbandry, that usually comes in farming, you can say part of the agricultural sector can be also managed by AI-enabled devices. Image annotation helps to detect and recognize the animals helping the farmers to keep an eye and monitor the live stocks making the animal husbandry business profitable. Bounding box annotation and polygon annotation helps to recognize the animals precisely.

AI systems can conduct chemical soil analyses and provide accurate estimates of missing nutrients. The type of soil and nutrition of soil plays an important factor in the type of crop is grown and the quality of the crop. It’s time to implement image recognition-based technology. that can identify the nutrient deficiencies in soil including plant pests and diseases by which farmers can also get an idea to use fertilizer which helps to improve harvest quality. The farmer can capture images of plants using smartphones. TagX can label these images for model training.

In the agricultural sector image annotation is also used for geo-sensing to check the soil condition and other attributes of the agricultural fields to analyze the situation to decide for the right timing of crop sowing and harvesting. Basically, drones are used and semantic segmentation image annotation technique is used to observe and monitor the health condition of the various agricultural fields.

With useful crops there are many unwanted crops that utilize the minerals of the soil under the roots, that should reach the main crop. Such unwanted plants are called weeds that should be removed to improve the crop yield of the crops and boost the productivity of the entire agricultural field.

In the future, AI will help farmers evolve into agricultural technologists, using data to optimize yields down to individual rows of plants. Artificial Intelligence in agriculture not only helps farmers to automate their farming but also shifts to precise cultivation for higher crop yield and better quality while using fewer resources.

Companies involved in improving machine learning or Artificial Intelligence-based products or services like training data for agriculture, drone, and automated machine making will get technological advancement in the future and will provide more useful applications to this sector helping the world deal with food production issues for the growing population.

 provides you with high-quality training data by integrating our human-assisted approach with machine-learning assistance. Our text, image, audio, and video annotations will give you the courage to scale your AI and ML models. Regardless of your data annotation criteria, our managed service team is ready to support you in both deploying and maintaining your AI and ML projects.

How crucial is Image Annotation for Agriculture to Adapt AI?

The process of adding annotations to videos is known as video annotation or video labelling. The primary goal of video annotation is to make it easier for computers to identify objects in videos using AI-powered algorithms. Annotated videos create a high-quality reference database that computer vision-enabled systems can use to accurately identify objects like cars, people, and animals. With an increasing number of everyday tasks relying on computer vision, the value of video annotation cannot be overstated.

Video Annotation is one of the annotation processes that requires labeling target objects in video footage. This information is generally added to videos by human annotators who apply outlines and labels to video frames in line with the specific requirements of each machine learning model.In most cases, video annotation means teams of annotators locating relevant objects in each frame of video data.

Most commonly, annotators use bounding boxes to pinpoint objects that machine learning engineers have designated as important to label. These boxes will then be assigned a colour and a label. Different machine learning projects require different ranges of objects to be labeled, in different ways.

While video annotation is useful for detecting and recognizing objects, its primary purpose is to create training data sets. When it comes to video annotation, there are several different steps that apply.

 – With frame-by-frame detection, individual items of interest are highlighted and categorized. By capturing specific objects, detection with ML algorithms can be improved.

 – object localization helps to identify specific images within a defined boundary. This helps algorithms find and locate the primary object in an image.

 – often used with autonomous vehicles, object tracking helps detect street lights, signage, pedestrians, and more to improve road safety.

 – similar to object tracking, individual tracking is focused on humans and how they move. Video annotation at sporting facilities helps ML algorithms understand human movement in different situations.

Bounding boxes are a video annotation technique in which annotators draw a box around a specific object or image in a video. The box is then annotated so that computer vision tools can automatically identify similar objects in videos. This is one of the most common methods of video annotation.

Cuboids are useful for marking up objects in three dimensions. We can describe the size, orientation, and location of an object in a frame using this form of annotation. It is especially helpful for annotating 3D-structured things like furniture and cars.

Unlike bounding box annotation, polygon annotation can be used to identify more complex objects. Any object, regardless of shape, can be annotated with a polygon annotation. This type of video annotation is ideal for objects with complex shapes, such as people and vehicles.

Images in videos are labelled using a variety of image annotation techniques. It has the ability to label certain parts of an image up to full segmentation. The semantic meaning of every pixel is tagged, enabling the computer vision model to operate at the highest level of accuracy.

Keypoints are quite helpful for video annotations if we don’t need to worry about the shape of an object. Key point annotation is commonly used to identify small objects, shapes, postures, and movements.

The traditional method of single image video annotation extracts each frame from the video and annotates each frame individually. The video is divided into frames, and each image is annotated in the traditional way. The target object’s annotated in every frame of the video. In complex scenarios, single frame annotation is always used because it ensures quality.

The continuous frame method of video annotation can be streamlined with automation technologies. Frame-by-frame tracking of objects and their locations can be done automatically by computers, maintaining the information’s continuity and flow. In order to assess the pixels in the previous and subsequent frames and forecast the motion of the pixels in the current frame, computers rely on continuous frame techniques like optical flow. With this amount of information, the computer can correctly identify an object that is visible at the start of the video, then vanishes for a number of frames before reappearing later. Teams can mistakenly identify that thing as a different object when it reappears if they instead utilise the single image method. This approach nevertheless has its share of difficulties. Low-resolution captured video, such as that used for surveillance, is possible. Engineers are working to develop interpolation technologies like optical flow to better utilise context across frames for object recognition in order to address this issue.

You can use interpolation. You don’t have to annotate every single frame with AI annotation tools. You can sometimes annotate the start and finish of your series and then interpolate between them. The annotations in between will be generated automatically.

The temporal context opens up new opportunities. Videos involve motion, which can be challenging to train for a static image-based AI model. By annotating videos, you can help the AI model learn how objects move and change over time.

Improved data for training AI models. Images are not as detailed as videos. Annotating a video gives the AI system more data to work with, which can lead to more accurate results.

It is cost efficient. A single video has more data points than a single image. And by concentrating on only a few keyframes, the entire process is less time consuming.

Video annotation plays a crucial role in training computer vision models. However, segmenting a video into small frames and annotating a piece separately with the right metadata, unavoidable data quality compliances, inherent linguistic complexities, numerous probable classifiers, and volumes of data certain video contains is challenging. Businesses, therefore, outsource video annotation services to get excellent results quickly and cost-efficiently.

TagX offers an efficient and accessible annotation framework that can be modified according to the deep learning model’s relevant use cases. Our professional annotators deliver the best-in-class results with the right blend of skills, experience, and expertise. Apart from the frame-by-frame analysis of videos, detection and metadata annotation, and object recognition, we also provide rapid video annotation services for computer vision models.

Video Annotation: A Complete Guide

Unmanned Aerial Vehicles (UAVs) have created great ease in a number of operations and across various industries. Integrating the latest technologies has allowed for higher levels of reliability and a more reassuring degree of trust in the use of UAVs. in the air. It also enables drones to capture and identify obstacles in real-time and dodge possible collisions. When deprived of computer vision, a drone only captures digital images and videos of the environment but doesn’t have the intelligence to understand and interact with its surroundings.

Computer vision is a field of artificial intelligence that trains computers to identify, interpret, and track objects in imagery and video. The technology is driven by pattern recognition. It’s trained by feeding computer models thousands to millions of images with labeled objects. This allows the algorithms to establish a profile (e.g., color, shape) for each object to then identify the things in unlabeled images.

Thanks to advances in machine learning and neural networks, computer vision has made great leaps in recent years and can often surpass the human eye in detecting and labeling certain objects. One of the driving factors behind this growth is the amount of data we generate that can be used to train computer vision models more accurately.

In many cases, machine learning and deep learning are also involved with computer vision algorithms, which increases the prediction level accuracy.

Industries advancing with Drones and Computer Vision

Many sectors resort to drones to get more work done in a shorter period of time. Here are some examples of industries that lean towards AI drone applications:

Numerous modern drone systems include real-time monitoring for improved security and on-the-spot analysis, decision-making, and planning. The creation of weekly progress maps is quicker, simpler, and less expensive thanks to drones than it would be otherwise. Additionally, they make it simpler and easier for construction companies to share information with their clients, improving communication and efficiency overall.

Drones provide high-resolution images that help field laborers feel less stressed. It is simple to map and survey the field and keep an eye on the plantation at all times. It also offers farmers precise data, allowing them to make well-informed decisions about pesticide application, water requirements, and soil health. The ability of drones to fly as high or as low as required makes it easier for farmers to identify potential issues that the human eye from ground level cannot see. Common applications in farming include:

3. Ripeness tracking and timing the harvest

Drone-based visual AI technology can be used in smart cities to detect and respond to a variety of problems more quickly, earlier, and more effectively. Cities can employ drone AI to monitor and keep an eye out for issues with transportation like traffic congestion, accidents, and slowdowns. This can facilitate faster first responder response times and give crucial telemetry data for enhancing citywide traffic flow and safety. Drone AI for smart cities may also keep an eye out for the warning signals of fires, flooding, and fire dangers. This enables cities to take preventative action or react more quickly to emergencies.

Security drones that are used for video surveillance can perform useful and essential tasks. Drones can monitor the job sites, record aerial footage of the assets, secure perimeters, and deter break-ins to supplement human guards. When combined with AI technology, they can provide real-time data streaming around the clock. Drones can automatically recognize things that pose dangers and notify security of the threat as AI makes them smarter. Additionally, they can be set up to take particular measures immediately if they discover harmful items, weapons, perimeter intrusion, or unusual activity. They can perform remote security operations and make quicker incident response decisions thanks to AI video analytics.

Users can routinely employ computer vision drones in an industrial or manufacturing scenario to continuously monitor for cracks and leaks in vital infrastructure items including pipes, storage towers, water tanks, gas tanks, and more. These devices have the ability to monitor crucial machine parts for the quick and early detection of flares and overheating. To check for certain requirements, they can fly through tunnels, mines, along pipes and power lines, or high above installations.

Drones are capable of quickly and efficiently inspecting difficult-to-reach sites, monitoring huge areas, and providing the data needed to evaluate potentially harmful situations. Drones enable better-informed decisions during bad occurrences because of their capacity to cover large regions regardless of topography, allowing them to reach closer to dangers like high voltage zones without placing people at risk of harm. An intelligent drone can fly over rapidly, spot dangers, and deliver aerial photos and live video. Security personnel can then gauge the level of danger and decide on the best course of action. Drones also come in handy to provide comprehensive coverage of the crime scene for officers to be fully briefed on the situation and assess the risk before venturing in to tackle the incident.

The best way to maximize accuracy and performance in drone technology is to improve your data labeled for aerial imaging. We’ve listed the most common data annotation and segmentation techniques to train computer vision models on drone data.

Bounding boxes are rectangles drawn over objects of interest in an image. 2D bounding box annotation can be used to train your computer vision segmentation algorithms to recognize, track, and avoid objects during flight.

2D bounding boxes aren’t enough to inform your drone about an object’s length, width, or depth. Bringing your average bounding box into the third dimension creates a cuboid. Cuboids are essential for orienting your drone in a real-world environment. Image annotation for self-driving cars relies heavily on cuboids for the same reason.

A real-world environment contains more than straight edges. To truly navigate on its own, a drone must be able to detect trees, lamp posts, fences, and rooftops. All of which can involve asymmetrical and irregular shapes. Polygon annotations provide more detail than bounding boxes and cuboids in practice, more detail translates to better drone vision. The most interesting part of polygon annotation is that it annotates the objects in irregular shapes providing the true detection of objects from an aerial view.

When it comes to machine learning image processing, image segmentation offers the most detail. Segmentation can be carried out as pixel-by-pixel annotation, considerably improving the visual accuracy of the environment that your drone will be flying over.

Particularly for detail-oriented activities like geo-sensing and monitoring deforestation, semantic segmentation is frequently used.

Video annotation for drone training helps to recognize moving objects while flying in the mid-air. Humans running, livestock moving, or vehicles driving fast can be only recognized by drones, if trained with the right training data created by drones.

Drones and other unmanned vehicles are becoming more and more common. The development of drone systems with enhanced and refined systems has been made possible by technological advances in sectors like blockchain, artificial intelligence, and machine learning. These systems give more security, safety, and efficiency to help drone missions succeed.

 is providing autonomous flying training data solutions with a wide range of image and video annotations for top aerial view in the images for drone mapping and imagery making drone training possible with highly accurate training data.

Computer Vision and Data Annotation: Leading the way for Drone AI

Voice assistants are devices/apps that respond to humans using voice recognition technology, natural language processing, and AI. The device uses technology to synthesis the user’s message, break it down, evaluate it, and respond with a logical response.

Unlike chatbots, voice bots require added speech recognition and speech synthesis abilities. Voice bots have to understand customer requests and determine the query’s intent. This critical factor directly impacts the accuracy of a voice bots response. To achieve this, voice bots synthesize a voice request, convert it into text for processing, and again deliver an accurate vocal response in return all within seconds.

Furthermore, voice bots must be able to access caller or user data to provide more pertinent and personalized responses; this data can includes the caller’s age, gender, purchase history, etc.

Have a deeper look into the article to know about the top reasons why voice bots should be an important part of your business.

In today’s world, everyone is drawn to innovation and wants to use it to make their lives easier, better, and richer. People are impatient, and they connect with a brand with the thought of now or never in mind. They only care about getting things done quickly, or they will switch to another service provider and never return.

A voice bot is the smartest solution for such people to get things done in a more personalized way. Due to their ability to provide timely support, AI voice bots can effectively handle customers. The customer expects a quick and secure answer for everything from trip reservations to online payments, and here is where voice bots can help.

In the age of intelligence and software revolutions, everything around us is getting virtualized or touch-free. AI voice bots offer new opportunities for personalization and intimate engagement for companies.

Unlike text-based solutions where the presence of a supporting device is a must, voice bots do not hook on any uncommon hardware devices. The advanced AI-based voice bot solution allows you to speak with a robot without using control buttons.

Machines, do not require rest. Even if your person is unavailable, voice bots can answer your customers’ questions and gather their information in the event of an emergency. And your agent can contact them whenever it is convenient for them.

For most consumers, calling any customer service center is a nightmare because of the long wait times and multiple redirections. Enabling FAQs on IVR, Alexa, or Google Assistant to automate general queries can save a lot of time, and the agent can take over or the call can only be transferred to the agent for important situations.

Having voice bots means utilizing a system with great data capabilities that can provide reliable insights to customers automatically. By providing real-time information, voice chatbots establish a seamless connection between customer and service agent and improve customer experience.

The best aspect of having a voice bot is that it effectively improves two-way communication, resulting in a pleasant and profitable experience. For example, a customer wishes to reserve a dinner table for him and his companion at their favorite restaurant. For this, he contacts the eatery.

The restaurant owner can improve services by including voice bots in the dialogue. Customers can ask questions using voice commands, and the smart-talking robot will respond. Corporate duties will be more easily managed in this manner, resulting in improved business efficiency and productivity.

One of the key things to note when using an artificially intelligent voice bot is to train the AI with a sufficiently large dataset of interactions with customers. Not every business might have access to well-documented support interactions that can be used as training material for the AI.

Algorithms learn from data. They find relationships, develop understanding, make decisions, and evaluate their confidence from the training data they’re given. And the better the training data is, the better the model performs.

Voicebot training data is information that helps a chatbot understand what users are saying and how to respond to it. To perform the training of these bots, the first step is to collect data according to model requirements. This Data needs to be annotated to attach labels to text or speech data.

These labels will train the model to respond effectively to different human interactions. Different types of NLP annotations required for Voice bots include

 offers specialized services for all annotation tasks related to Voice bot. 

 provides you with high-quality training data by integrating our human-assisted approach with machine-learning assistance. Regardless of your data annotation criteria, our managed service team is ready to support you in both deploying and maintaining your AI and ML projects.

As a closing statement, we would like to say that Modern AI voice bots can deal with plenty of simple cases and most of the customers don’t even realize they are interacting with a robot. And, it doesn’t mean the voice bots can replace humans. But a team of super-efficient bots and sharp-witted humans would be a perfect match.

AI-powered Voice bots: Importance and Training

Workers at construction and mining sites deal with a variety of duties that necessitate surveillance and supervision on a daily basis. They must also ensure that their construction tasks are carried out safely on site. While artificial intelligence and augmented reality are altering the business, computer vision and data annotation have the potential to fix some of the industry’s current problems and revitalize the sector. Some of the major issues in construction and mining include struck‐by accidents, continuous monitoring of unsafe conditions, quality and defect inspection, monitoring of site activities, and more.

Computer vision has enormous potential in the construction and mining industries. It can examine video footage from worksites in real-time, identify poor craftmanship, divergence from standardized work plans, and compare work done against BIM standards, thanks to its object identification and recognition capability. In terms of safety, it can monitor security camera footage and detect hardhats, high visibility vests, work goggles, shoes, and even special protection belts required for workers working at high altitudes. In case it observes the absence of protective gear, PPE compliance breach, or an impending threat, the system can also alert site managers to take action to save lives.

The quality of live stream video footage from construction and mining sites can be examined to find obvious flaws or problems. This early detection of flaws and quality issues can help projects save time, money, and resources. Following that, computer vision can assist in the creation of 3D-built models for progress monitoring, mapping, autonomous robotics, and presentations. This can aid in the planning and execution of construction projects.

Companies can also deploy drones, equipping them with LIDAR and HD cameras for workers and inventory monitoring. By annotating and analyzing the data collected from the drone, managers can track and transform these analytics into valuable insights to optimize the ongoing processes. For instance, computer vision can be leveraged to create spaghetti diagrams, identifying worker movement trajectories. This enables checking for longer travel paths, movement bottlenecks and optimize onsite material storage. Thus reducing the idle time and saving additional delay costs. This can also address the under-utilization of resources, lack of insights on activity, optimal coordination, and real-time intelligence.

Mining engineers and workers can use computer vision to assist reduce accidents and injuries on the job. We can identify failures that will not only damage productivity but also be hazardous or fatal to any workers nearby if enough high-quality data is collected and annotated.

We can utilize Computer Vision and data annotation to forecast other potential dangers in addition to failures by examining patterns in events. This is extremely beneficial because the environment in mining can have a significant impact on equipment functioning and lifespan, which varies substantially based on location.

However, AI can be used for more than only predicting when equipment will fail or which threats would arise. We can also monitor the health and performance of the equipment on a regular basis, which is critical for avoiding unexpected breakdowns and worker hazards.

Material management is a crucial component of project management since it accounts for the bulk of cost input in building and mining. Improper material management throughout a project might result in significant and needless expenses. Cement, for example, is frequently lost on building sites due to inefficient storage and handling. Workers, in particular, tend to use only resources that are proximate to their work area, resulting in the waste of additional materials kept on higher levels.

With computer vision, you can reduce the amount of material wasted. Tracking items stored on-site can be made easier by annotating data. This will not only assist you in improving project performance by identifying underutilized materials, but it will also assist you in reducing material waste and achieving better cost control over time.

Companies can undertake remote inspections of their premises and assets using autonomous vehicles or drones. The construction and mining industries have jumped ahead with a raft of technologies now available to make them more efficient, safer, and autonomous, thanks to a slew of new technologies that have emerged in the last ten years, including autonomous vehicles, trains, aircraft, and even autonomous mines.

This allows them to map shallow and deep features at higher resolutions than before, allowing them to have a better grasp of an area’s geology and completely evaluate it before drilling any needless deep holes. Operators have been able to minimize normal site visits by half by utilizing autonomous vehicles to inspect well sites.

Automation, as well as the application of AI and Machine Learning, may clearly help businesses save money, boost efficiency, and reap a variety of other benefits. What’s holding us back is a lack of excellent data and a vast quantity of it. Companies need high-quality labelled data for training in order to implement these algorithms.

 offers data annotation services for machine learning. Having a diverse pool of accredited professionals, access to the most advanced tools, cutting-edge technologies, and proven operational techniques, we constantly strive to improve the quality of our client’s AI algorithm predictions.

Companies are working on scaling the use of AI in mining and construction and with Computer vision becoming a huge industry we can start seeing more use of AI in the mining and construction industry. One that is completely changing from what we’ve traditionally known it to be. 

 can provide services to help you through the process of implementing such systems into your business.

Data Annotation to enable AI in Construction and mining

Teams must decide what type of data annotation is right for their application. This is an important question because data labeling can be expensive and time-consuming, but it is critical to the model’s success. So, teams are stuck with an often complicated cost-benefit analysis when it comes time to annotate their data. While it might be tempting to settle for image classification – it’s probably the cheapest and easiest to achieve – its applications are very limited. If we think about an autonomous vehicle computer vision model looking out onto a complex urban environment, we begin to see that just recognizing whether there is a human in its sight or not will not be enough. To avoid running the person over, the car also needs to know where the human is. If we take a medical computer vision application – identifying the shape of cancerous cells, we need instance segmentation, to differentiate between different instances of cells. Defining the whole image as “cells” won’t help us localize the problematic cells or to understand the extent of any problems.

But there are many cases where it’s not obvious what type of data annotation you need. This is a high-risk decision for teams. If they use the wrong annotation method or add the wrong information to their images, their model may not work and they’ll need to start the data labeling process anew. Simulated Data can relieve a lot of the stress associated with this type of decision by automatically and flexibly adding a wider range of annotations with perfect ground truth, but more on this later.

Once you’ve chosen your annotation method, there are even more choices to make; now you have to select an annotation technique. This is the actual method that annotators will use to attach annotations to your data. For instance, they may draw squares around objects, do multi-sided polygons, or attach landmarks. It is important to understand these techniques because, again, there is often a tradeoff between cost, time, and effectiveness.

– The most basic type of data annotation. It consists of drawing a rectangle or square around the target object and is very commonly used, due to its simplicity and versatility. This is useful when objects are relatively symmetrical – such as boxes of foods or road signs – or when the exact shape of the object is of less interest. On the other hand, complex objects don’t have right angles, and achieving ground truth annotation using bounding boxes is impossible. Additionally, we have no annotations for what’s happening “inside” the box. For instance, if we care about a person’s movement, posture, gait, or other dynamic indicators, bounding boxes are unlikely to be helpful.

 – a variation of the bounding box technique. By using complex shapes (polygons) and not only the right angles of bounding boxes, the target object’s location, and boundaries are defined more accurately. Increased accuracy cuts out irrelevant pixels that can confuse the classifier. This is good for more irregular shaped objects – cars, people, and logos, animals. While polygons are more accurate than bounding boxes, overlapping objects may be captured within a single polygon and therefore not distinguishable from each other.

 – this plots continuous lines made of one or more segments. It is best used when important features have a linear appearance. This is common in an autonomous vehicle context, as it is easily applied to define lanes and sidewalks. But, for most use cases this simply isn’t relevant because the object is not linear and more than a single pixel wide.

 – This is also known as dot annotation. It involves creating dots across the image. These small dots help detect and quantify characteristics in the data. It is used often in facial recognition to detect facial features, emotions, and expressions. It can be used to help annotate human bodies, align posture, and explore the relationship between different body parts. Another interesting use case is to find objects of interest in aerial footage such as cars, buildings, and more. Clearly, this approach is both time consuming and prone to inaccuracy. For instance, manually landmarking facial features like irises across thousands of images is very difficult to do consistently and accurately.

– This is a data labeling technique used to plot an object’s movement across multiple frames. Some tools include interpolation, which enables the annotator to label one frame, skip frames, and then annotate the new position. The annotating tools automatically fill in the movement and track the objects through the frames. While this is great theoretically, it takes a lot of work and high levels of accuracy to successfully annotate. In general, the cost of annotating video data quickly becomes cost prohibitive because of the need to annotate frame-by-frame.

Often, your use case will dictate the technique that’s right for you. But, even if you have little choice in which technique to adopt, it is critical to be aware of the constraints of each one. Expensive techniques may limit the amount of data you want to collect. While techniques with inherent variation may force you to pay extra attention to the effects of minor inconsistencies on your model’s performance.

Now, you’ve gathered your data and decided on the method and techniques of data annotation that work best for your model. It’s time to get the annotations added to your images. This annotating process involves people sitting, and manually marking image after image. Ideally, you might be assisted by some automation tools, but in general, it is a manual and labor-intensive process. In today’s annotation landscape, there are a couple of different solutions available to you:

– Crowdsourcing involves paying workers often distributed globally and working as freelancers to perform a micro-task or assignment. They are generally paid a small sum based on the volume of work they complete. Crowdsourced labor tends to be of low quality and consistency for obvious reasons. The workers are lightly vetted or may have little idea of what they are doing or common pitfalls. The burden of managing them falls on you. There are also platforms that crowdsource work but manage the workflow and sourcing of workers. Additionally, quality assurance post labeling requires resources and validation and without it is impossible to guarantee high-quality results. Because these tend to be one-off relationships, there is no feedback loop with the people working on your project and there is no way to train them over time. Data security is also a challenge as these people are often working independently on unsecured computers.

– Some companies choose to try and solve data annotation needs in-house. For small, easy-to-annotate datasets, this may be a great option. But, many companies often assign this low-level work to their data scientists and engineers, which is not a good use of their time. The alternative of hiring annotators in-house – which brings benefits of process control and QA carries significant overhead costs. Generally, this method is not scalable, as you invest in hiring, managing, and training employees while your data needs may fluctuate wildly over time. Teams that try to automate these processes or build in-house tech solutions often find themselves distracting valuable development teams with projects that would be more efficient to outsource.

– There are many data labeling companies often based in low-cost markets like India – that employee teams focused on data annotation. Some suppliers leverage certain ML models to accelerate the process and do QA. By virtue of employing the annotators, these companies are better able to control quality, can improve quality over time as they learn about your specific needs, and can provide better time estimates than the other options. But, ultimately this is still a manual process and any cost-savings come from the cheap cost of labor. You still have to devote operational resources to managing this relationship and, at the end of the day, you are still dependent on a third-party vendor that is subject to all kinds of delays, inconsistencies, and challenges.

As you can see, all of these options have significant operational, quality control, and process challenges. They generally force you to devote time and energy to things outside your core technological mandate.

Since data annotation is very important for the overall success of your AI projects, you should carefully choose your service provider. 

We have experts in the field who understand data and its allied concerns like no other. We could be your ideal partners as we bring to table competencies like commitment, confidentiality, flexibility and ownership to each project or collaboration. So, regardless of the type of data you intend to get annotations for, you could find that veteran team in us to meet your demands and goals. Get your AI models optimized for learning with us.

Which Type of Data Annotation Is Right For You? How to Get it done?

To compensate for one negative customer experience and rebuild the customer-organization relationship, it takes ten positive ones. According to Gartner, 85 percent of all customer contacts will be handled without the need of humans. Self-service technology like AI chatbots, device guides, decision trees, and others free up call center workers to focus on more difficult tasks rather than answering repetitive client inquiries. Automation is present in every business, including the customer service industry.

Artificial Intelligence for Contact Centers

Advancements in digital technology continue to transform customer service interactions across industries. With AI today, complex customer queries are no longer a burden. Build a virtual assistant that allows your customers to easily search databases to find the answers they are looking for, eliminating the need to dig through standard operating procedures.

Furthermore, answering client queries more quickly and accurately allows agents to work smarter while lowering support costs. It allows you to directly resolve your customers’ questions on the front end, while also providing your agents with the information and resources they require on the back end.

End-to-end customer service with AI, enabling improvements in loyalty and brand reputation to new revenue streams. Real-time self-service and AI assistance in the customer service industry bring huge opportunities to forward-thinking businesses.

Automation can deliver a level of responsiveness that businesses could not even think otherwise. In the future, virtual assistants will be able to predict what your customers are looking for by analyzing how users are interacting with your brand. If your intelligent virtual assistant has an idea of who your customers really are, it will be able to deliver answers way before customers even know that they have concerns.

Advantages of Customer Service Automation

In an average service call, if you notice 75% of that time, agents are doing manual research while actual human customer interaction is limited to only 25%. This delay can be avoided entirely if AI could be used to go through company servers, previous occurrences of the related problem, and come up with a detailed guide or step-by-step solution.

Without a doubt, the quicker a problem is solved, the happier your customers will be. In fact, according to a study, 39% of customers try to solve a problem themselves by visiting the FAQ page first, before seeking the help of customer service. The customer service desk can speed up service multifold by developing a virtual assistant to handle customer queries on the front end.

If the customer service desk can solve a problem before it even arises, it makes for a really happy and satisfied customer. AI has huge potential to analyze customer data, past interactions with agents, and identify frequent customer issues and problems by monitoring websites, and in-app activity for distress indicators.

Enabling automated customer service has the capabilities to respond to your customer’s queries within minutes. Customers don’t want to wait around for several hours or days for a response.

Connecting with your customers on a personal level even while using automation is the key factor to growth. A study showed that 42% of customers become repeat customers after a good customer service experience, whilst 52% stopped their purchases after a single poor customer service interaction.

Automation removes or we can say minimizes the human element in this area of your customer service, which boosts the potential for idleness, futile effort, and human error.

On the business end, on average, it takes $5000 to train a customer service agent, not to mention the monthly salary which you have to provide. On the flip side, AI needs to be trained once and it can be trusted to remember every one of its lessons.

The simplest and most common queries can be handled by automated responses, FAQs, and step-by-step text as well as visual guides. However, for more serious and complicated support requests, you can implement a knowledge base software that enables self-service across all touchpoints.

When a customer request is transferred to a new employee, the agent should get the records of the conversation that the customer was already having, along with probable solutions and guides. Customers don’t like having to repeat their problems to a second or third person. According to a report by Capterra, 72% of customers blame a poor customer experience on having to explain their problem multiple times.

Because most companies have multiple communication channels, including instant chat on website and app, email, query submission pages, and phone automation, it’s mandatory to have a unified platform where all the data regarding a single query gets compiled, regardless of the channel it came in through. This reduces duplication of effort for both the customer as well as a service agent.

An intelligent automation platform can redefine end-to-end customer interactions effectively too at a lower cost. This enables companies to create greater efficiencies, lower down customer handling time, and fully automate their business operations end-to-end. Thus, the way forward, a key consideration should be implementing a solution that integrates business processes with artificial intelligence.

The Era of AI-powered Customer Engagement is Now

Insurance firms frequently outsource insurance data entry services to alleviate their obstacles, headaches, and big issues. Their large volume of papers must be processed in a timely, correct, secure, and cost-effective manner, which is why they rely on specialized services that provide all of these advantages.

These services have the best technology, infrastructure, and professional employees to deal with large amounts of data on a daily basis. It assists insurance clients in increasing their overall business efficiency and focusing on their core competencies, as well as making informed decisions. This was made possible by the streamlined support provided by third-party companies.

Hiring such offshore enterprises ensures that insurance clients obtain the appropriate core expertise, saving them time, effort, and operating costs. The outcomes of these outsourcing services are exact and high-quality, with the best security measures in place to ensure sensitive data is protected.

The reasons why insurance companies outsource insurance data entry services are as follows:

The proficient data entry services staff in offshore companies utilize optimum workflows that always ensure clients receive highly accurate outcomes. The reliable scrutiny procedures are done with progressive technologies in multi-tier setups and with the best manual supervision.

The third-party or offshore companies provide round-the-clock support to all clients with their personnel working in different shifts. Even if the projects are complicated or enormous, they will ensure to add additional resources to get it done efficiently and on time. They have rapid turnaround times for all their processes gained through their years of experience and industry exposure.

The massive, qualified personnel strength and best-of-breed technology of offshore firms ensure insurance companies receive their voluminous projects at their stipulated time frames. There won’t be any compromise in the data quality and accuracy levels even when the turnaround time is rapid.

Client companies can save their investments in infrastructure, staffing, and technology costs with outsourcing as third-party companies already have all these in place. These services are reasonably-priced even though they offer results in the highest-quality and accuracy levels. As a result, insurance organizations can save on their operational expenses by great figures which can be pooled into other core areas.

The state-of-the-art technologies deployed offshore are lined with the stringent security features that ensure total protection and confidentiality to sensitive information from all kinds of cyber threats, breaches, or data leaks. They also have stringent backup mechanisms to protect this information from all kinds of loss or disasters. Even complete confidentiality contracts such as non-disclosure agreements (NDAs) are signed with clients before project commencement.

These are the highly favorable reasons as to why insurance companies prefer outsource insurance data entry services rather than doing it within their organization. It eliminates all their hassles with processing delays, inaccurate and inconsistent data, security concerns, and more. Availability of these solutions will exponentially boost their productivity and efficiency levels in the long run. The added benefit of cost-effectiveness will help them gain substantial profits, better ROIs, and stable revenues.

, we provide quality insurance data entry services and a series of other insurance services to global clients. Our cost-effective services help clients to save money and time, which can be invested in improving other core competencies. Whether you need insurance claims data entry services or data mining services, our dedicated team can assist you with all your business’ basic and data entry requirements. Outsource insurance data analytics services and data entry services for insurance to us and take advantage of all benefits we offer.

5 Reasons Insurance benefits from Data Entry Outsourcing

Autonomous vehicles are still working towards reaching the stage of full autonomy. A fully functioning and safe autonomous vehicle must be competent in a wide range of machine learning processes before it can be trusted to drive on its own. From processing visual data in real-time to safely coordinating with other vehicles, the need for AI is essential. Self-driving cars could not do any of this without a huge volume of different types of training data, created and tagged for specific purposes.

Due to the several existing sensors and cameras, advanced automobiles generate a tremendous amount of data. We cannot use these datasets effectively unless they are correctly labeled for subsequent processing. This could range from simple 2D bounding boxes all the way to more complex annotation methods, such as semantic segmentation.

There are various image annotation types such as Polygons, bounding boxes, 3D cuboids, Semantic Segmentation, Lines, and Splines that can be incorporated into autonomous vehicles. These annotation methods help in achieving greater accuracy for autonomous driving algorithms. However, which annotation method is best suited for you must be chosen according to the requirements of your project.

Types of Annotation for Autonomous Driving

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