“A Learning Method for Object Detection from Low Resolution Image”
Yash Munot1, Mrunalinee Patole2, Chetan Jadhav3, Abhijeet Raut4, Namita Rode5

1Yash Munot, Department Of Computer Engineering R.M.D. Sinhgad School Of Engineering, Warje, Pune.
2Mrunalinee Patole, Department Of Computer Engineering R.M.D. Sinhgad School Of Engineering, Warje, Pune.
3Chetan Jadhav , Department Of Computer Engineering R.M.D. Sinhgad School Of Engineering, Warje, Pune.
4Abhijeet Raut, Department Of Computer Engineering R.M.D. Sinhgad School Of Engineering, Warje, Pune.
5Namita Rode, Department Of Computer Engineering R.M.D. Sinhgad School Of Engineering, Warje, Pune.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 3992-3995 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8991038620/2020©BEIESP | DOI: 10.35940/ijrte.F8991.038620

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Object recognition the use deep neural networks has been most typically used in real applications. We propose a framework for identifying items in pics of very low decision through collaborative studying of two deep neural networks. It includes photo enhancement network object popularity networks. The picture correction community seeks to decorate images of much lower decision faster and more informative images with the usages of collaborative gaining knowledge of indicatores from object recognition networks. Object popularity networks actively participate in the mastering of photograph enhancement networks, with skilled weights for photographs of excessive resolution. It uses output from photograph enhancement networks as augmented studying recordes to reinforce the overall performance of its identity on a very low decision object. We esablished that the proposed method can improve photograph reconstruction and classification overall performance.
Keywords: Image Acquisition, Image Segmentation, Feature Extraction, Artificial Neural Network.
Scope of the Article: Smart Learning Methods and Environments.