The challenge with object detection is localization of objects of varying sizes, types and multiple perspective angles. Once objects are detected they can be marked using bounding boxes or on pixel level using segmentation.

Object detection is a field of computer vision with a vast number of use cases, from region localization to pixel level localization of objects are catered in object detection. Objects can be of any shape or size or from any domain. Some examples of object detection are food detection, person detection, animal detection, vehicle detection, QA in industries etc.

Our approach for an object detection problem is to provide an end to end R&D phase, ranging from data generation and collection to development of the final Artificial Intelligence algorithm and its optimization for deployment whether its on edge devices or on cloud. We use state of the art data generation techniques to eliminate the problem of not having enough data for Artificial Intelligence model training.

The benefits of using object detection for any client can range from process optimizations in terms of time to cost cutdown in terms of reduction of manual labor.

ByteCorp worked on developing the technology and the solution by first identifying the limitations of existing and competitor system. Interviews were conducted with potentials customers to pin point he exact requirements, and then a R&D cycle was initiated to develop the solution.