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Wu Tianshu, Zhang Zhijia, Liu Yunpeng, Guo Wanyan, Wang Zitao. Driver Seat Belt Detection Based on YOLO Detection and Semantic Segmentation[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(1): 126-131. DOI: 10.3724/SP.J.1089.2019.17244
Citation: Wu Tianshu, Zhang Zhijia, Liu Yunpeng, Guo Wanyan, Wang Zitao. Driver Seat Belt Detection Based on YOLO Detection and Semantic Segmentation[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(1): 126-131. DOI: 10.3724/SP.J.1089.2019.17244

Driver Seat Belt Detection Based on YOLO Detection and Semantic Segmentation

  • 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.
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