Safety helmet wearing detection algorithm based on improved YOLOv5

doi:10.11772/j.issn.1001-9081.2021071246

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (4): 1292-1300.DOI: 10.11772/j.issn.1001-9081.2021071246

Special Issue: CCF第36届中国计算机应用大会 (CCF NCCA 2021)

• The 36 CCF National Conference of Computer Applications (CCF NCCA 2020) • Previous Articles Next Articles

Safety helmet wearing detection algorithm based on improved YOLOv5

Jin ZHANG¹, Peiqi QU¹(), Cheng SUN², Meng LUO²

^1.College of Information Science and Engineering，Hunan Normal University，Changsha Hunan 410081，China
^2.School of Mathematics and Statistics，Hunan Normal University，Changsha Hunan 410081，China

Received:2021-07-16 Revised:2021-08-27 Accepted:2021-08-31 Online:2021-09-10 Published:2022-04-10
Contact: Peiqi QU
About author:ZHANG Jin， born in 1979， Ph. D.， professor. His research interests include artificial intelligence， software engineering.
SUN Cheng， born in 1994， Ph. D. candidate. Her research interests include artificial intelligence， computer vision.
LUO Meng， born in 1996， M. S. candidate. Her research interests include artificial intelligence， computer vision.
Supported by:
National Defence Basic Scientific Research Program of State Administration of Science, Technology and Industry for National Defence(WDZC20205500119);Natural Science Foundation of Hunan Province(2021JJ30456);Science and Technology Progress and Innovation Program of Department of Transportation of Hunan Province(201927);Open Project of State Key Laboratory of Industrial Control Technology(ICT2021B10);Hunan Province Graduate Student Training Innovation Practice Base Project （Xiang Jiao Tong ［2019］ 248）

基于改进YOLOv5的安全帽佩戴检测算法

张锦¹, 屈佩琪¹(), 孙程², 罗蒙²

^1.湖南师范大学信息科学与工程学院，长沙 410081
^2.湖南师范大学数学与统计学院，长沙 410081

通讯作者: 屈佩琪
作者简介:张锦（1979—），男，河南信阳人，教授，博士，CCF会员，主要研究方向：人工智能、软件工程
孙程（1994—），女，山东枣庄人，博士研究生，CCF会员，主要研究方向：人工智能、计算机视觉
罗蒙（1996—），女，江西南昌人，硕士研究生，CCF会员，主要研究方向：人工智能、计算机视觉。
基金资助:
国防科工局国防基础科研计划项目(WDZC20205500119);湖南省自然科学基金资助项目(2021JJ30456);湖南省交通运输厅科技进步与创新计划项目(201927);工业控制技术国家重点实验室开放课题(ICT2021B10);湖南省研究生培养创新实践基地项目(湘教通〔2019〕248号)

Abstract

Abstract:

Aiming at the problems of strong interference and low detection precision of the existing safety helmet wearing detection， an algorithm of safety helmet detection based on improved YOLOv5 （You Only Look Once version 5） model was proposed. Firstly， for the problem of different sizes of safety helmets， the K-Means++ algorithm was used to redesign the size of the anchor box and match it to the corresponding feature layer. Secondly， the multi-spectral channel attention module was embedded in the feature extraction network to ensure that the network was able to learn the weight of each channel autonomously and enhance the information dissemination between the features， thereby strengthening the network ability to distinguish foreground and background. Finally， images of different sizes were input randomly during the training iteration process to enhance the generalization ability of the algorithm. Experimental results show as follows： on the self-built safety helmet wearing detection dataset， the proposed algorithm has the mean Average Precision （mAP） reached 96.0%， the the Average Precision （AP） of workers wearing safety helmet reached 96.7%， and AP of workers without safety helmet reached 95.2%. Compared with the YOLOv5 algorithm， the proposed algorithm has the mAP of helmet safety-wearing detection increased by 3.4 percentage points， and it meets the accuracy requirement of helmet safety-wearing detection in construction scenarios.

Key words: safety helmet wearing detection, object detection, deep learning, YOLOv5 (You Only Look Once version 5), attention mechanism

摘要：

针对现有安全帽佩戴检测干扰性强、检测精度低等问题，提出一种基于改进YOLOv5的安全帽检测新算法。首先，针对安全帽尺寸不一的问题，使用K-Means++算法重新设计先验框尺寸并将其匹配到相应的特征层；其次，在特征提取网络中引入多光谱通道注意力模块，使网络能够自主学习每个通道的权重，增强特征间的信息传播，从而加强网络对前景和背景的辨别能力；最后，在训练迭代过程中随机输入不同尺寸的图像，以此增强算法的泛化能力。实验结果表明，在自制安全帽佩戴检测数据集上，所提算法的均值平均精度（mAP）达到96.0%，而对佩戴安全帽的工人的平均精度（AP）达到96.7%，对未佩戴安全帽的工人的AP达到95.2%，相较于YOLOv5算法，该算法对佩戴安全帽的平均检测准确率提升了3.4个百分点，满足施工场景下安全帽佩戴检测的准确率要求。

关键词: 安全帽佩戴检测, 目标检测, 深度学习, YOLOv5, 注意力机制

CLC Number:

TP391.41

Jin ZHANG, Peiqi QU, Cheng SUN, Meng LUO. Safety helmet wearing detection algorithm based on improved YOLOv5[J]. Journal of Computer Applications, 2022, 42(4): 1292-1300.

张锦, 屈佩琪, 孙程, 罗蒙. 基于改进YOLOv5的安全帽佩戴检测算法[J]. 《计算机应用》唯一官方网站, 2022, 42(4): 1292-1300.

Figures/Tables 15

References 33

1	常欣，刘鑫萌. 建筑施工人员不合理佩戴安全帽事故树分析［J］. 吉林建筑大学学报， 2018， 35（6）：65-69. 10.3969/j.issn.1009-0185.2018.06.014
	CHANG X， LIU X M. Fault tree analysis of unreasonably wearing helmets for builders［J］. Journal of Jilin Jianzhu University， 2018， 35（6）：65-69. 10.3969/j.issn.1009-0185.2018.06.014
2	王忠玉. 智能视频监控下的安全帽佩戴检测系统的设计与实现［D］. 北京：北京邮电大学， 2018： 1-18.
	WANG Z Y. Design and implementation of detection system of wearing helmets based on intelligent video surveillance［D］. Beijing： Beijing University of Posts and Telecommunications， 2018：1-18.
3	GIRSHICK R， DONAHUE J， DARRELL T， et al. Rich feature hierarchies for accurate object detection and semantic segmentation［C］// Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2014：580-587. 10.1109/cvpr.2014.81
4	GIRSHICK R. Fast R-CNN［C］// Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway： IEEE， 2015：1440-1448. 10.1109/iccv.2015.169
5	REN S Q， HE K M， GIRSHICK R， 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. 10.1109/tpami.2016.2577031
6	REDMON J， DIVVALA S， GIRSHICK R， et al. You only look once： unified， real-time object detection［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2016：779-788. 10.1109/cvpr.2016.91
7	LIU W， ANGUELOV D， ERHAN D， et al. SSD： single shot multiBox detector［C］// Proceedings of the 2016 European Conference on Computer Vision， LNCS 9905. Cham： Springer， 2016：21-37.
8	REDMON J， FARHADI A. YOLO9000： better， faster， stronger［C］// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2017：6517-6525. 10.1109/cvpr.2017.690
9	REDMON J， FARHADI A. YOLOv3： an incremental improvement［EB/OL］. （2018-04-08）［2021-04-08］.. 10.1109/cvpr.2018.00430
10	BOCHKOVSKIY A， WANG C Y， LIAO H Y M. YOLOv4： optimal speed and accuracy of object detection［EB/OL］. ［2020-05-09］.. 10.1109/cvpr46437.2021.01283
11	Ultralytics. YOLOv5［CP/OL］. ［2020-08-09］.. 10.1109/iccvw54120.2021.00312
12	ZHANG L L， LIN L， LIANG X D， et al. Is faster R-CNN doing well for pedestrian detection？［C］// Proceedings of the 2016 European Conference on Computer Vision， LNCS， 9906. Cham： Springer， 2016：443-457. 10.1007/978-3-319-46475-6_28
13	宋欢欢，惠飞，景首才，等.改进的RetinaNet模型的车辆目标检测［J］.计算机工程与应用，2019，55（13）：225-230.
	SONG H H， HUI F， JING S C， et al. Improved RetinaNet model for vehicle target detection［J］. Computer Engineering and Applications， 2019， 55（13）：225-230.
14	陈磊，张孙杰，王永雄.基于改进的YOLOv3及其在遥感图像中的检测［J］.小型微型计算机系统，2020，41（11）：2321-2324. 10.3969/j.issn.1000-1220.2020.11.014
	CHEN L， ZHANG S J， WANG Y X. Based on improved YOLOv3 and its detection in remote sensing images［J］. Journal of Chinese Computer Systems， 2020， 41（11）：2321-2324. 10.3969/j.issn.1000-1220.2020.11.014
15	邓壮来，汪盼，宋雪桦，等.基于SSD的粮仓害虫检测研究［J］. 计算机工程与应用，2020，56（11）：214-218.
	DENG Z L， WANG P， SONG X H， et al. Research on granary pest detection based on SSD［J］. Computer Engineering and Applications， 2020， 56（11）：214-218.
16	张海川，彭博，许伟强.基于UNet++及条件生成对抗网络的道路裂缝检测［J］.计算机应用，2020，40（S2）：158-161.
	ZHANG H C， PENG B， XU W Q. Road crack detection based on UNet++ and conditional generative adversarial nets［J］. Journal of Computer Applications， 2020， 40（S2）：158-161.
17	KELM A， LAUβAT L， MEINS-BECKER A， et al. Mobile passive Radio Frequency Identification （RFID） portal for automated and rapid control of Personal Protective Equipment （PPE） on construction sites［J］. Automation in Construction， 2013， 36：38-52. 10.1016/j.autcon.2013.08.009
18	刘晓慧，叶西宁.肤色检测和Hu矩在安全帽识别中的应用［J］.华东理工大学学报（自然科学版），2014，40（3）：365-370.
	LIU X H， YE X N. Skin color detection and Hu moments in helmet recognition research［J］. Journal of East China University of Science and Technology （Natural Science Edition）， 2014， 40（3）：365-370.
19	SHRESTHA K， SHRESTHA P P， BAJRACHARYA D， et al. Hard-hat detection for construction safety visualization［J］. Journal of Construction Engineering， 2015， 2015：No.721380. 10.1155/2015/721380
20	RUBAIYAT A H M， TOMA T T， KALANTARI-KHANDANI M， et al. Automatic detection of helmet uses for construction safety［C］// Proceedings of the 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops. Piscataway： IEEE，2016：135-142. 10.1109/wiw.2016.045
21	SILVA R R V e， AIRES K R T， VERAS R de M S. Helmet detection on motorcyclists using image descriptors and classifiers［C］// Proceedings of the 27th SIBGRAPI Conference on Graphics， Patterns and Images. Piscataway： IEEE， 2014：141-148. 10.1109/sibgrapi.2014.28
22	李琪瑞.基于人体识别的安全帽视频检测系统研究与实现［D］.成都：电子科技大学，2017：1-6，34-59.
	LI Q R. A research and implementation of safety-helmet video detection system based on human body recognition［D］. Chengdu： University of Electronic Science and Technology of China， 2017： 1-6， 34-59.
23	WU H， ZHAO J S. An intelligent vision-based approach for helmet identification for work safety［J］. Computers in Industry， 2018， 100：267-277. 10.1016/j.compind.2018.03.037
24	QIN Z Q， ZHANG P Y， WU F， et al. FcaNet： frequency channel attention networks［EB/OL］. （2021-07-23）［2021-08-04］.. 10.1109/iccv48922.2021.00082
25	HU J， SHEN L， SUN G. Squeeze-and-excitation networks［C］// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2018：7132-7141. 10.1109/cvpr.2018.00745
26	WANG Q L， WU B G， ZHU P F， et al. ECA-net： efficient channel attention for deep convolutional neural networks［C］// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2020：11531-11539. 10.1109/cvpr42600.2020.01155
27	FU J， LIU J， TIAN H J， et al. Dual attention network for scene segmentation［C］// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2019：3141-3149. 10.1109/cvpr.2019.00326
28	WOO S， PARK J， LEE J Y， et al. CBAM： convolutional block attention module［C］// Proceedings of the 2018 European Conference on Computer Vision， LNCS 11211. Cham： Springer， 2018：3-19.
29	ZHANG Z L， ZHANG X Y， PENG C， et al. ExFuse： enhancing feature fusion for semantic segmentation［C］// Proceedings of the 2018 European Conference on Computer Vision， LNCS 11214. Cham： Springer， 2018：273-288.
30	CHAIB S， LIU H， GU Y F， et al. Deep feature fusion for VHR remote sensing scene classification［J］. IEEE Transactions on Geoscience and Remote Sensing， 2017， 55（8）：4775-4784. 10.1109/tgrs.2017.2700322
31	GHIASI G， LIN T Y， LE Q V. NAS-FPN： learning scalable feature pyramid architecture for object detection［C］// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2019：7029-7038. 10.1109/cvpr.2019.00720
32	PENG D Z， SUN Z K， CHEN Z R， et al. Detecting heads using feature refine net and cascaded multi-scale architecture［C］// Proceedings of the 24th International Conference on Pattern Recognition. Piscataway： IEEE， 2018：2528-2533. 10.1109/icpr.2018.8545068
33	EVERINGHAM M， WINN J. The PASCAL Visual Object Classes challenge 2012 （VOC2012） development kit［EB/OL］. （2012-05-18）［2021-07-20］.. 10.1007/s11263-009-0275-4

特征图尺度	锚框尺寸
特征图尺度	锚框1	锚框2	锚框3
小尺度	（11.9，18）	（21.5，30.8）	（30.8，43）
中尺度	（38.1，60）	（52.3，73.6）	（63，103.3）
大尺度	（89.2，135）	（120，207.5）	（209.4，324）

特征图尺度	锚框尺寸
特征图尺度	锚框1	锚框2	锚框3
小尺度	（11.9，18）	（21.5，30.8）	（30.8，43）
中尺度	（38.1，60）	（52.3，73.6）	（63，103.3）
大尺度	（89.2，135）	（120，207.5）	（209.4，324）

K-Means++聚类	MCA模块	AP50/%		mAP/%
K-Means++聚类	MCA模块	佩戴安全帽	未佩戴安全帽	mAP/%
×	×	93.3	91.7	92.7
√	×	94.4	92.8	93.6
×	√	95.6	94.4	95.0
√	√	96.7	95.2	96.0

K-Means++聚类	MCA模块	AP50/%		mAP/%
K-Means++聚类	MCA模块	佩戴安全帽	未佩戴安全帽	mAP/%
×	×	93.3	91.7	92.7
√	×	94.4	92.8	93.6
×	√	95.6	94.4	95.0
√	√	96.7	95.2	96.0

检测算法	AP50			精度	召回率	mAP
检测算法	小目标	中等目标	大目标	精度	召回率	mAP
YOLOv5	83.0	97.9	99.3	76.4	92.5	92.7
MCA-YOLOv5-BackBone	90.4	98.6	99.6	82.2	95.4	96.0
MCA-YOLOv5-Neck	78.3	96.4	99.1	70.9	93.7	91.6
MCA-YOLOv5-Prediction	82.7	97.1	99.2	72.5	92.8	92.4

Safety helmet wearing detection algorithm based on improved YOLOv5

基于改进YOLOv5的安全帽佩戴检测算法

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Figures/Tables 15

References 33

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Metrics

目标类别	训练集目标数	测试集目标数	标注目标总数
佩戴安全帽类别	81 836	11 316	93 152
未佩戴安全帽类别	98 187	12 021	110 208

类别	条目	版本
硬件配置	显卡	GeForce RTX 2080 Ti
软件配置	系统	Ubuntu 18.04
	CPU	AMD Ryzen 7 3800X 8-Core
	Python版本	3.8
	深度学习框架	Pytorch
	CUDA	10.0

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检测算法	AP50/%		mAP/%	参数量/10⁶	推理时间/ms	模型大小/MB
检测算法	佩戴安全帽	未佩戴安全帽	mAP/%	参数量/10⁶	推理时间/ms	模型大小/MB
Faster R-CNN	80.80	42.20	61.50	186.00	291	182.1
SSD	78.80	68.20	73.50	23.75	126	188.0
YOLOv3	89.12	80.70	84.90	61.90	69	236.0
YOLOv3+SPP	90.50	86.30	88.41	63.00	70	237.4
YOLOv5	93.30	91.70	92.70	7.10	36	14.8
MCA-YOLOv5	96.70	95.20	96.00	7.30	37	15.2

检测算法	AP50/%		mAP/%	参数量/10⁶	推理时间/ms	模型大小/MB
检测算法	佩戴安全帽	未佩戴安全帽	mAP/%	参数量/10⁶	推理时间/ms	模型大小/MB
Faster R-CNN	80.80	42.20	61.50	186.00	291	182.1
SSD	78.80	68.20	73.50	23.75	126	188.0
YOLOv3	89.12	80.70	84.90	61.90	69	236.0
YOLOv3+SPP	90.50	86.30	88.41	63.00	70	237.4
YOLOv5	93.30	91.70	92.70	7.10	36	14.8
MCA-YOLOv5	96.70	95.20	96.00	7.30	37	15.2