A complete solution for detecting objects using AI and machine learning in images
By Anolytics | 04 July, 2019 in Image Annotation | 5 mins read
AI and machine learning are completely relying on training data to develop a model for real-life application. The training data directly relates to a properly labeled supervised data basically available in annotated image form for computer vision object detection.
Without such data it is not possible to train the model and get the accurate prediction. Finding such labeled data for different types of industry model training becomes hard for AI and ML developers. But image annotation companies like Anolytics.ai came with the complete image annotation solution for wide-ranging industries as per the needs. Here, we will discuss about what Anolytics covers, which industries and what types of image annotation services offered.
Image Annotation for Self-driving or Autonomous Vehicles
While walking on the roads you can see multiple types of objects like cars, traffic lights, sign boards, speed breakers, street signs, poles including living beings like humans and animals. And autonomous vehicles need to detect every object comes on the street, so that it can change its position or control the speed and direction accordingly.
To make such objects recognizable for computer vision in self-driving certain types of annotated data sets in the from of images are feed into the machine learning algorithms. All such objects are annotated using various types of annotation techniques like bounding box, semantic segmentation, polygon annotation, 3D cuboid or 3D point cloud annotation and polyline annotation to train the different types of perception model detect the objects and lanes by the autonomous driving vehicles for safe driving.
Anolytics.ai provides the complete annotation solution for self-driving car and autonomous vehicles training. Understanding the actual needs of the clients, it can annotate the huge amount of data required for self-driving model training while ensuring the accuracy and quality to make sure clients can develop a fully-functional AI model at low cost.
Image Annotation for Healthcare and Medical Imaging Data
AI applications in Healthcare is becoming more prevalent with use in new sub-fields like diagnosing the life-threatening diseases or provide the better medical care facilities. Actually, from the image annotation perspective the medical imaging solutions with annotated images like X-rays, MRI and CT Scans to make it recognizable for machines to detect the diseases.
Polygons, semantic segmentation, bounding boxes and point annotation are used to annotate the images in detecting the illness in kidney, liver, brain, teeth and bones. Even cancer cells can be also annotated to train the AI-supported machines learn from such patterns and predict the similar symptoms if used in real-life for detecting deadly diseases.
Analytics is providing the high-quality image annotation services for medical imaging with best level of accuracy. It has qualified and well-trained radiologists to manually annotate such images using the cutting-edge tools and techniques to annotate precisely helping AI developers to utilize the objection detection technology for computer vision.
Image Annotation Solution for Retail and Ecommerce
In ecommerce and retail, AI and machine learning is providing the better shopping experience. Many times you get the recommended items while browsing internet on browser or visiting at any ecommerce website, this process works in machine learning technology that shows the results as per your browsing history on various devices.
Similarly, visual search and search relevance are the AI-supported process used in searching the right items on ecommerce websites helping people to buy items as per their means at less efforts. In retail inventory management categorized times kept on the rack is detected through AI-enabled machines or robots are trained to detect the right parcels at warehousing with full automation of logistic and supply chain management.
Anolytics a one of the emerging data annotation company can provide the high-quality annotated images for retail and ecommerce industry. It can annotate related images in bounding boxes or 2D, 3D cuboids and other forms of annotation allowing AI developers to develop the right perception model and automate the retail management.
Image Annotation for Automated Machines and Robotics
Robotics working on machine learning or AI-enabled perception model are trained with supervised labeled data sets containing the annotated objects in warehousing and inventory management. Image annotation solution helps robots to learn the types of objects come at warehouses and detect them when processed at such places.
Bounding box, 3D cuboid, semantic segmentation and other popular forms of image annotation is used to detect the objects with precise locations and pick or drop them without facing any issue. An accurately training data for robotics can help AI companies to develop the fully-functional robots that can manage inventory at faster pace.
Anolytics is providing image annotation services for robotics with quality annotated images with a fully scalable work environment to deal with a large volume of data sets. It is working with a team of highly-skilled and experienced annotators to perform the task and complete the requirements of clients working on AI-based robots development.
Image Annotation for Robots and Aerial Imagery in Agriculture
Agriculture is another sector where AI is playing a crucial role in increasing crop yield with better productivity at a low cost. Actually, in the agricultural sector automated machines including robots and drones are used to monitor the crop’s health, detect plant’s diseases and remove unwanted crops helping farmers to boost productivity and do precise farming.
AI-based tractors are used for the cultivation of an entire field while on the other hand AI-enabled robots used in agriculture or farming are trained with annotated images of crops, vegetables, fruits and unwanted weeds to take action accordingly. These robots are trained to harvest fruits or vegetables as per their fructify condition means whether it is ready for harvesting or not and detect unwanted flora eating the nutrition of main crops etc.
Similarly, drones are also trained to capture images of fields and live stocks like animals browsing grass on the open ground. These drones can record video of the entire field that helps to monitor the health of soil and crops helps in controlling the various types of pest infestations with predictive analysis of farming for farmers like optimum planting, water management, crop rotation, timely harvesting, nutrient management and pest attacks.
Analytics offers image annotation outsourcing for companies working on models AI-based autonomous machines or robotics used in agriculture and farming. From bounding box to semantic segmentation, Anolytics can provide wide-ranging image annotation solutions for AI or machine learning model training for the agriculture sector.
Image Annotation for Autonomous Flying and Drone Training
Autonomous flying like drones are nowadays highly used in security surveillance and object detection at various locations where humans cannot reach easily. These autonomous flying modulation perceive the environment from areal view and detect the objects of interest with the capability to process the live data anywhere with connected systems.
2D aerial view imagery mapping with 2D bounding box annotation or semantic segmentation for Geo sensing in agriculture through drones can be done only when such annotated data is fed into the model. Polygons for object localization, bounding box for human tracking and object detection are popular annotation types used in drones training.
Anolytics provides the complete training data solution for autonomous flying and drones with all types off feasible annotation techniques. It is working with advance business model to perform such tasks with best quality. It is working with fully scalable solution and data security providing the accurate image annotation solution at cost-effective pricing.
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