Driver Seat Belt Detection Based on YOLO Detection and Semantic Segmentation

被引：0
|
作者：
Wu T. [1 ,2 ]
Zhang Z. [1 ]
Liu Y. [2 ]
Guo W. [1 ]
Wang Z. [1 ]
机构：
[1] School of Software, Shenyang University of Technology, Shenyang
[2] Department of Optical-Electronics and Information Processing, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 01期
关键词：
Deep learning; Seat belt detection; Semantic segmentation; Traffic video surveillance;
D O I：
10.3724/SP.J.1089.2019.17244
中图分类号：
学科分类号：
摘要：
In order to detect whether a driver wears a seat belt automatically through traffic monitor, a driver's seat belt detection algorithm based on object detection and semantic segmentation was proposed. Firstly, a lightweight target detection algorithm was designed to locate the driver's area quickly. Then, the driver's area was segmented by the semantic segmentation model accelerated with pruning, and the connected area of the seat belt was obtained. Finally, the area of the connected area of the seat belt was judged to detect whether the driver worn the seat belt. The speed of the algorithm is 305 frames/s when the accuracy is 94.87%. The experimental results show that the algorithm has good accuracy while taking into account the speed. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
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页码：126 / 131
页数：5
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