Vehicle Detection from Images using Deep Fully Convolutional Networks
Vengoti Bhargavi1, D. Rajeswara Rao2
1Vengoti Bhargavi, Studying Master of Technology, Department of Computer Science and Engineering, V R Siddhartha Engineering College Vijayawada.
2D. Rajeswara Rao, Professor, Department of Computer Science and Engineering, V R Siddhartha Engineering College, Vijayawada.
Manuscript received on 04 March 2019 | Revised Manuscript received on 09 March 2019 | Manuscript published on 30 July 2019 | PP: 3176-3180 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3103078219/19©BEIESP | DOI: 10.35940/ijrte.B3103.078219
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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: Vehicle detection provides the facilitation of traffic planning and management. It also helps in finding suspected and stolen vehicles. Although it has many applications, it is a very complex problem due to variations in vehicle type and size. As the amount of vehicle types are very high the models find it hard to classify the correct type of the vehicle. In this paper, we are proposing a vehicle detection model based on YoloV3 convolutional neural network architecture with custom backbone. Our proposed backbone in the Yolov3 architecture helps classify the different types of vehicles accurately. This makes the classification of the images at pixel level and predicts the regression based ROI bounding box for the classified vehicles in the images. The model contains features extracted at different kernel sizes to find the features at multiple scales which will then be concatenated. Experiments were performed on the Kitti vehicle detection dataset have shown the superior performance of our proposed model.
Index Terms: Image Classification, Regression, Vehicle Detection, Yolov3
Scope of the Article: Classification