Development of object identification model with deep reinforcement learning algorithm

This research work presents an object identification method based on the machine learning technique based on human vision system. The objective is to prevent processing a complete image in sort to locate objects. Presently, the-state-of-the-art techniques divide an image into sub-regions and search for an object in all the subparts. This is ineffective for applications like embedded systems where the computation power is restricted or the resolution of the images are high. To address this issue, an object identification task was formulated as a decision-making problem. Followed the concept of DRL proposed, accepted RL algorithm DQL was applied to learn a policy from input data, i.e. images, to identify objects in a scene. In this manner, with the policy learned, a set of actions that transforms a box was apply in order to make tighter a bounding box around the target object.