Multi-Scale Detector for Accurate Vehicle Detection in Traffic Surveillance Data
| Autor: | Doo-Hyun Choi, Pyong-Kun Kim, Kwang-Ju Kim, Yun-Su Chung |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Computer science UA-DETRAC benchmark 02 engineering and technology 01 natural sciences Convolutional neural network Field (computer science) Pyramid 0202 electrical engineering electronic engineering information engineering General Materials Science Computer vision Pyramid (image processing) business.industry Deep learning 010401 analytical chemistry Detector General Engineering deep learning object detection neural networks traffic surveillance Object detection 0104 chemical sciences machine learning Feature (computer vision) Benchmark (computing) 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 7, Pp 78311-78319 (2019) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2019.2922479 |
| Popis: | The recent research by deep learning has shown many breakthroughs with high performance that were not achieved with traditional machine learning algorithms. Particularly in the field of object detection, commercial products with high accuracy in the real environment are applied through the deep learning methods. However, the object detection method using the convolutional neural network (CNN) has a disadvantage that a large number of feature maps should be generated in order to be robust against scale change and occlusion of the object. Also, simply raising the number of feature maps does not improve performance. In this paper, we propose to integrate additional prediction layers into conventional Yolo-v3 using spatial pyramid pooling to complement the detection accuracy of the vehicle for large scale changes or being occluded by other objects. Our proposed detector achieves 85.29% mAP, which outperformed than those of the DPM, ACF, R-CNN, CompACT, NANO, EB, GP-FRCNN, SA-FRCNN, Faster-R CNN2, HAVD, and SSD-VDIG on the UA-DETRAC benchmark data-set consisting of challenging real-world-traffic videos. |
| Databáze: | OpenAIRE |
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