Vehicle Classification and Detection using Deep Learning
V. Vijayaraghavan1, M. Laavanya2
1V. Vijayaraghavan, Department of Electronics and Communication Engineering, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur (Andhra Pradesh), India.
2M. Laavanya, Department of Electronics and Communication Engineering, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur (Andhra Pradesh), India.
Manuscript received on 23 November 2019 | Revised Manuscript received on 17 December 2019 | Manuscript Published on 30 December 2019 | PP: 24-28 | Volume-9 Issue-1S5 December 2019 | Retrieval Number: A10061291S52019/19©BEIESP | DOI: 10.35940/ijeat.A1006.1291S519
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Intelligent transportation systems have acknowledged a ration of attention in the last decades. In this area vehicle classification and localization is the key task. In this task the biggest challenge is to discriminate the features of different vehicles. Further, vehicle classification and detection is a hard problem to identify and locate because wide variety of vehicles don’t follow the lane discipline. In this article, to identify and locate, we have created a convolution neural network from scratch to classify and detect objects using a modern convolution neural network based on fast regions. In this work we have considered three types of vehicles like bus, car and bike for classification and detection. Our approach will use the entire image as input and create a bounding box with probability estimates of the feature classes as output. The results of the experiment have shown that the projected system can considerably improve the accuracy of the detection.
Keywords: Convolutional Neural Network, Object Detection, Deep Learning, Image Classification.
Scope of the Article: Deep Learning