Open Access
Research Article
Development of object identification model with deep reinforcement learning algorithm
P. Ramesh Naiduramesh.naidu@nmit.ac.inDepartment of Computer Science and Engineering Nitte Meenakshi Institute of TechnologyDepartment of Computer Science and Engineering Nitte Meenakshi Institute of Technology Nitte (Deemed to be University)Bangalore, Karnataka, 560064, IndiaView full profile → , Avinash Sharmaasharma@mmumullana.orgDepartment of Computer Science & Engineering M. M. Engineering College Maharishi Markandeshwar (Deemed To Be) UniversityMullana, Ambala, IndiaView full profile → , Supriya P. Diwansupriya.diwan8@gmail.comDepartment of Electronics and Telecommunication Engineering Government College of EngineeringKarad, Satara, Maharashtra, IndiaView full profile → , *V. Dankan GowdaCorresponding authordankan.v@bmsit.inDepartment of Electronics and Communication Engineering BMS Institute of Technology and ManagementBangalore, Karnataka, 560119, IndiaView full profile → , Parth M. Pandyapandyaparth05@gmail.comDepartment of Mathematics Indus UniversityAhmedabad, Gujarat, IndiaView full profile → , Anand Kumar GuptaGanand40@yahoo.co.inDepartment of Information Technology BlueCrest UniversityMonrovia, LiberiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 01 Apr 2023
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1346
- Pages:
- 355–367
Abstract
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.
Keywords
Subject Classifications
Primary 93A30Secondary 49K1593B3068Q87
References
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