改进YOLOv5s的安全帽佩戴检测算法研究

doi:10.3778/j.issn.1002-8331.2305-0237

摘要/Abstract

摘要： 针对安全帽佩戴目标检测算法参数多、推理速度慢以及检测准确率低等问题，提出基于改进YOLOv5s的安全帽佩戴检测算法研究。在主干网络添加坐标注意力机制（coordinate attention，CA），提高模型对关键特征的注意力，更聚焦训练安全帽相关目标特征，提高准确率；在特征提取网络引入结构重参数化技术（RepVGG），并在颈部网络融合鬼影混洗卷积（ghost-shuffle conv，GSConv）和VoV-GSCSP构造Slim-neck，在保证模型检测精度和泛化能力的同时，大幅降低模型参数量；设计使用SIoU优化边界框回归损失函数，提升预测框准确度和加快收敛速度。结果表明：改进算法的速度较原始YOLOv5s模型提高了49.51%，参数大小压缩了75.03%，平均精度均值提高了0.029，具有更好效果。

关键词: 安全帽检测, YOLOv5s, 坐标注意力机制, 结构重参数化

Abstract: Aiming at the problems of multiple parameters, slow reasoning speed and low detection accuracy of safety helmet wearing target detection algorithm, a research on safety helmet wearing detection algorithm based on improved YOLOv5s is proposed. Firstly, a coordinate attention mechanism（CA） is added to the backbone network to improve the model’s attention ability to key features, focus more on training helmet-related target features, and improve accuracy. Secondly, the structural reparameterization technology（RepVGG） is introduced in the feature extraction network, and the ghost-shuffle conv（GSConv） and VoV-GSCSP are integrated into the neck network to construct a Slim-neck, which greatly reduces the number of model parameters while ensuring the accuracy of model detection and generalization ability. Finally, SIoU is used to optimize the bounding box regression loss function to improve the prediction box accuracy and accelerate the convergence speed. The results show that compared with the original YOLOv5s model, the speed of the improved algorithm is increased by 49.51%, the parameter size is compressed by 75.03%, and the average accuracy is increased by 0.029, which has better results.

Key words: safety helmet detection, YOLOv5s, coordinate attention mechanism, structural reparameterization

刘雅洁, 伊力哈木·亚尔买买提, 席凌飞, 英特扎尔·艾山江. 改进YOLOv5s的安全帽佩戴检测算法研究[J]. 计算机工程与应用, 2023, 59(20): 184-191.

LIU Yajie, Yilihamu·Yaermaimaiti, XI Lingfei, Yingtezhaer·Aishanjiang. Research on Improved Safety Helmet Wearing Detection Algorithm of YOLOv5s[J]. Computer Engineering and Applications, 2023, 59(20): 184-191.

参考文献

[1] 张立艺，武文红，牛恒茂，等.深度学习中的安全帽检测算法应用研究综述[J].计算机工程与应用，2022，58（16）：1-17.
ZHANG L Y，WU W H，NIU H M，et al.Summary of application research on helmet detection algorithm based on deep learning[J].Computer Engineering and Applications，2022，58（16）：1-17.
[2] 曹文刚.基于深度学习的工地安全防护用具检测[D].南京：南京邮电大学，2021：17-18.
CAO W G.Deep learning-based inspection of site safety equipment[D].Nanjing：Nanjing University of Posts and Telecommunications，2021：17-18.
[3] REN S Q，HE K M，SUN J，et al.Faster R-CNN：towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2017，39（6）：1137-1149.
[4] 张博，宋元斌，熊若鑫，等.融合人体关节点的安全帽佩戴检测[J].中国安全科学学报，2020，30（2）：177-182.
ZHANG B，SONG Y B，XIONG R X，et al.Helmet-wearing detection considering human joint[J].China Safety Science Journal，2020，30（2）：177-182.
[5] REDMON J.DIVVALA S，GIRSHICK R，et al.You onlylook once：unified，real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition（CVPR），2016：779-788.
[6] 王兵，李文璟，唐欢.改进YOLO v3算法及其在安全帽检测中的应用[J].计算机工程与应用，2020，56（9）：33-40.
WANG B，LI W J，TANG H.Improved YOLOv3 algorithm and its application in helmet detection[J].Computer Engineering and Applications，2020，56（9）：33-40.
[7] 赵红成，田秀霞，杨泽森，等.YOLO-S：一种新型轻量的安全帽佩戴检测模型[J].华东师范大学学报（自然科学版），2021（5）：134-145.
ZHAO H C，TIAN X X，YANG Z S，et al.YOLO-S：a new lightweight helmet wearing detect on model[J].Journal of East China Normal University（Natural Science），2021（5）：134-145.
[8] SANDLER M，HOWARD A，ZHU M，et al.Mobilenetv2：inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：4510-4520.
[9] 杨永波，李栋.改进YOLOv5的轻量级安全帽佩戴检测算法[J].计算机工程与应用，2022，58（9）：201-207.
YANG Y B，LI D.Lightweight helmet wearing detection algorithm of improved YOLOv5[J].Computer Engineering and Applications，2022，58（9）：201-207.
[10] HOWARD A，SANDLER M，CHU G，et al.Searching for mobilenetv3[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：1314-1324.
[11] DING X，ZHANG X，MA N，et al.RepVGG：making VGG-style convnets great again[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2021：13733-13742.
[12] 李明山，韩清鹏，张天宇，等.改进SSD的安全帽检测方法[J].计算机工程与应用，2021，57（8）：192-197.
LI M S，HAN Q P，ZHANG T Y，et al.Safety helmet detection method of improved SSD[J].Computer Engineering and Applications，2021，57（8）：192-197.
[13] 许德刚，王露，李凡.深度学习的典型目标检测算法研究综述[J].计算机工程与应用，2021，57（8）：10-25.
XU D G，WANG L，LI F.Review of typical object detection algorithms for deep learning[J].Computer Engineering and Applications，2021，57（8）：10-25.
[14] HOU Q B，ZHOU D Q，FENG J S.Coordinate attention for efficient mobile nrtwork design[C]//2021 IEEE/CVF Conference on computer Vision and Pattern Recongnition.New York：IEEE Press，2021：13708-13717.
[15] SONG H.Multi-scale safety helmet detection based on RSSE-YOLOv3[J].Sensors，2022，22（16）：6061.
[16] LI H，LI J，WEI H，et al.Slim-neck by GSConv：a better design paradigm of detector architectures for autonomous vehicles[J].arXiv：2206.02424，2022.
[17] ZHENG Z，WANG P，REN D，et al.Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J].IEEE Cybernetics，2021，52（8）：8574-8586.
[18] GEVORGYAN Z.SIoU Loss：more powerful learning for bounding box regression[J].arXiv：2205.12740，2022.