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结合YOLO检测和语义分割的驾驶员安全带检测
Alternative TitleDriver Seat Belt Detection Based on YOLO Detection and Semantic Segmentation
吴天舒1,2; 张志佳1; 刘云鹏2; 郭婉妍1; 王子韬1
Department光电信息技术研究室
Source Publication计算机辅助设计与图形学学报
ISSN1003-9775
2019
Volume31Issue:1Pages:126-131
Indexed ByEI ; CSCD
EI Accession number20191206655090
CSCD IDCSCD:6410612
Contribution Rank1
Funding Organization国家自然科学基金(61540069) ; 装发部共用技术课题项目(Y6k4250401)
Keyword安全带检测 语义分割 交通视频监控 深度学习
Abstract为了通过交通监控自动检测驾驶员是否佩戴安全带,提出一种结合目标检测与语义分割的驾驶员安全带检测算法.首先通过设计轻量化目标检测算法完成驾驶员区域快速定位;然后利用经过剪枝加速的语义分割模型对驾驶员区域进行分割,得出安全带连通域;最后通过判断安全带连通域面积检测驾驶员是否佩戴安全带.在驾驶员区域定位和安全带检测2个数据集上进行训练和测试,实验结果表明,驾驶员区域定位算法在精准度为99.96%时速度为73帧/s,安全带检测算法在准确率为94.87%时速度为305帧/s;该算法在兼顾速度的同时具有较好的精准度.
Other AbstractIn 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.
Language中文
Citation statistics
Cited Times:1[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24102
Collection光电信息技术研究室
Corresponding Author张志佳
Affiliation1.沈阳工业大学软件学院
2.中国科学院沈阳自动化研究所光电信息技术研究室
Recommended Citation
GB/T 7714
吴天舒,张志佳,刘云鹏,等. 结合YOLO检测和语义分割的驾驶员安全带检测[J]. 计算机辅助设计与图形学学报,2019,31(1):126-131.
APA 吴天舒,张志佳,刘云鹏,郭婉妍,&王子韬.(2019).结合YOLO检测和语义分割的驾驶员安全带检测.计算机辅助设计与图形学学报,31(1),126-131.
MLA 吴天舒,et al."结合YOLO检测和语义分割的驾驶员安全带检测".计算机辅助设计与图形学学报 31.1(2019):126-131.
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