[논문]Multi-Scale Detector for Accurate Vehicle Detection in Traffic Surveillance Data

Kim, Kwang-Ju; Kim, Pyong-Kun; Chung, Yun-Su; Choi, Doo-Hyun

doi:10.1109/access.2019.2922479

Multi-Scale Detector for Accurate Vehicle Detection in Traffic Surveillance Data 원문보기

IEEE access : practical research, open solutions, v.7, 2019년, pp.78311 - 78319

Kim, Kwang-Ju (Electronics and Telecommunications Research Institute, Daegu, South Korea) , Kim, Pyong-Kun (Electronics and Telecommunications Research Institute, Daegu, South Korea) , Chung, Yun-Su (Electronics and Telecommunications Research Institute, Daegu, South Korea) , Choi, Doo-Hyun (School of Electronics Engineering, Kyungpook National University, Daegu, South Korea)

Abstract ▼ AI-Helper

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.

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Multi-Scale Detector for Accurate Vehicle Detection in Traffic Surveillance Data 원문보기

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Multi-Scale Detector for Accurate Vehicle Detection in Traffic Surveillance Data 원문보기

Abstract ▼ AI-Helper

참고문헌 (32)

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