Open AccessA Learning Method for Object Detection from Low Resolution Image
Author(s) -
Yash Munot*,
Mrunalinee Patole,
Chetan C. Jadhav,
Abhijeet Raut,
Namita Rode
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8991.038620
Subject(s) - popularity , computer science , artificial intelligence , object (grammar) , artificial neural network , cognitive neuroscience of visual object recognition , deep learning , object detection , computer vision , image (mathematics) , machine learning , low resolution , identity (music) , pattern recognition (psychology) , high resolution , geography , psychology , physics , acoustics , remote sensing , social psychology
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.