2D Bounding box annotation service for precise object detection through computer vision to train the AI and machine learning models. Cogito bestow bounding box annotated images as a training data with next level of accuracy for multiple industries to advance the object recognition of visual perception models.
Object Detection for Self-driving Cars
The best use of bounding box annotation technique is to train the AI-enabled autonomous vehicles ADAS-supported cars that can recognize the different types of objects on the road. 2D Bounding box annotation techniques create a training data for self-driving models to detect the objects like traffic signals, potholes, lanes, traffic and pedestrians etc. All these objects on road can be labeled with bounding box annotation.
Image Tagging for Ecommerce and Retail
Image bounding box annotation also visualizes the items sold at online store or retail shops. Visual perception models trained with bounding box machine learning able to recognize objects like fashion accessories, furniture and other items with right labeling. Actually, it aids visual search technology to detect the objects picked from the store and generate automatic billing without human help.
Damage Detection for Insurance Claims
Vehicles damaged in accidents or in other mishaps can be easily identified with bounding box deep learning technique. Damaged vehicle body, roof, front or tail lights, dented bonnet, broken glasses and other accessories of a vehicle can be made recognizable to Computer Vision. It also helps machine learning to estimate the level of damage, certifying insurance companies for appropriate claims.
Precise Bounding Box Annotation by Cogito
Apart from autonomous vehicle, ecommerce and insurance claim visual perception model training data, Cogito also provides the high-quality image annotation service for autonomous flaying training, satellite imagery, robotics in agriculture and healthcare sector. Strengthened by a team of well-trained annotators, we commit to deliver an advanced level of training data for AI and ML backed with bounding box annotation techniques.
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