改进YOLOv5s算法的安全帽佩戴检测

doi:10.3778/j.issn.1002-8331.2206-0468

摘要/Abstract

摘要： 佩戴安全帽是施工过程中人员安全的重要保障之一，但现有的人工检测不仅耗时耗力而且无法做到实时监测，针对这一现象，提出了一种基于深度学习的安全帽佩戴检测算法。该算法以YOLOv5s网络为基础。在网络的主干网中引入CoordAtt坐标注意力机制模块，考虑全局信息，使得网络分配给安全帽更多的注意力，以此提升对小目标的检测能力；针对原主干网对特征融合不充分的问题，将主干网中的残差块替换成Res2NetBlock结构中的残差块，以此提升YOLOv5s在细粒度上的融合能力。实验结果表明：在自制的安全帽数据集中验证可知，与原有的YOLOv5算法相比，平均精度提升了2.3个百分点，速度提升了18 FPS，与YOLOv3算法相比，平均精度提升了13.8个百分点，速度提升了95 FPS，实现了更准确的轻量高效实时的安全帽佩戴检测。

关键词: 安全帽佩戴检测, YOLOv5s, CoordAtt, Res2NetBlock

Abstract: Wearing safety helmet is one of the important guarantees for personnel safety in the construction process. However, the existing manual detection is time-consuming and unable to achieve real-time monitoring. Aiming at this phenomenon, a safety helmet detection algorithm based on deep learning is proposed. The algorithm is based on YOLOv5s network. Firstly, CoordAtt coordinate attention mechanism module is introduced in the backbone network of the network, considering the global information, so that the network can allocate more attention to the safety helmet, so as to improve the detection ability of small targets. Secondly, to solve the problem of insufficient feature fusion in the original backbone network, the residual block in the backbone network is replaced by the residual block in the Res2NetBlock structure, so as to improve the fine-grained fusion ability of YOLOv5s.?The experimental results show that：It can be seen from the verification in the self-made helmet data set that, compared with the original YOLOv5 algorithm, the average accuracy is improved by 2.3 percentage points and the speed is increased by 18 FPS. Compared with the YOLOv3 algorithm, the average accuracy is improved by 13.8 percentage points and the speed is increased by 95 FPS. A more accurate lightweight and efficient real-time helmet wearing detection is realized.

Key words: helmet wearing detection, YOLOv5s, CoordAtt, Res2NetBlock

宋晓凤, 吴云军, 刘冰冰, 张青林. 改进YOLOv5s算法的安全帽佩戴检测[J]. 计算机工程与应用, 2023, 59(2): 194-201.

SONG Xiaofeng, WU Yunjun, LIU Bingbing, ZHANG Qinglin. Improved YOLOv5s Algorithm for Helmet Wearing Detection[J]. Computer Engineering and Applications, 2023, 59(2): 194-201.

参考文献

[1] NAJIBI M，SAMANGOUEI P，CHELLAPPA R，et al.SSH：single stage headless face detectorn[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：4875-4884.
[2] CHEN Q，TANG S，YANG Q，et al.Cooper：cooperative perception for connected autonomous vehicles based on 3d point clouds[C]//International Conference on Distributed Computing Systems，2019：514-524.
[3] GIRSHICK R，DONAHUE J，DARRELL T，et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2014：580-587.
[4] GIRSHICK R.FAST R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision，2015：1440-1448.
[5] REN S，HE K，GIRSHICK R，et al.Faster R-CNN：towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems，2015：91-99.
[6] RUBAIYA A，TOMA T T，KAlANTARI-KHANDANI M，et al.Automatic detection of helmet uses for construction safety[C]//IEEE/WIC/ACM International Conference on Web Intelligence Workshops，2017.
[7] 孙国栋，李超，张航.融合自注意力机制的安全帽佩戴检测方法[J].计算机工程与应用，2022，58（20）：300-304.
SUN G D，LI C，ZHANG H.Safety helmet wearing detection method fused with self-attention mechanism[J].Computer Engineering and Applications，2022，58（20）：300-304.
[8] LIU W，ANGUELOV D，ERHAN D，et al.SSD：single shot multibox detector[C]//European Conference on Computer Vision.Cham：Springer，2016.
[9] REDMON J，DIVVALA S，GIRSHICK R，et al.You only look once：Unified，real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：779-788.
[10] REDMON J，FARHADI A.YOLO9000：better，faster，stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：7263-7271.
[11] REDMON J，FARHADI A.Yolov3：an incremental improvement[J].arXiv：1804.02767，2018.
[12] BOCHKOVSKIY A，WANG C Y，LIAO H Y M.Yolov4：optimal speed and accuracy of object detection[J].arXiv：2004.10934，2020.
[13] 张勇，吴孔平，高凯，等.基于改进型YOLOv3安全帽检测方法的研究[J].计算机仿真，2021，38（5）：413-417.
ZHANG Y，WU K P，GAO K，et al.Helmet detection based on modified YOLOv3[J].Computer Simulation，2021，38（5）：413-417.
[14] 王雨生，顾玉宛，庄丽华，等.复杂姿态下的安全帽佩戴检测方法研究[J].计算机工程与应用，2022，58（1）：190-196.
WANG Y S，GU Y W，ZHUANG L H，et al.Research on detection method of helmet wearing in complex posture[J].Computer Engineering and Applications，2022，58（1）：190-196.
[15] 王英郦.基于YOLOv5的施工现场安全帽佩戴监测算法[J].信息技术与信息化，2022（7）：33-36.
WANG Y L.Construction site helmet wearing monitoring algorithm based on YOLOv5[J].Information Technology and Informatization，2022（7）：33-36.
[16] WANG C Y，LIAO H Y M，WU Y H，et al.CSPNet：a new backbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops，2020：390-391.
[17] LIU S，QI L，QIN H，et al.Path aggregation network for instance egmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：8759-8768.
[18] YU J，JIANG Y，WANG Z，et al.Unitbox：an advanced object detection network[C]//Proceedings of the 24th ACM International Conference on Multimedia，2016：516-520.
[19] REZATOFIGHI H，TSOI N，GWAK J Y，et al.Generalized intersection over union：a metric anda loss for bounding box regression[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：658-666.
[20] ZHENG Z，WANG P，LIU W，et al.Distance-IoU loss：faster and better learning for bounding box regression[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2020：12993-13000.
[21] HU J，SHEN L，SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：7132-7141.

[22] WOO S，PARK J，LEE J Y，et al.CBAM：convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：3-19.

[23] GAO S H，CHENG M M，ZHAO K，et al.Res2net：a new multi- scale backbone architecture[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2019，43（2）：652-662.