| Citation: |
WANG Yuanbin，WEI Sixiong，WU Huaying，et al. Detection algorithm for wearing safety helmet under mine based on improved YOLOv5s[J]. Coal Science and Technology，2025，53（S1）：366−377. DOI: 10.12438/cst.2023-1839
|
Aiming at the problems of low accuracy and high missed detection rate of personnel safety helmet detection algorithm caused by complex environment under mine, an improved mine safety helmet detection algorithm based on YOLOv5s is proposed. Due to the computer system, the global context information of the image is easily lost when the convolutional neural network extracts the features, resulting in poor detection effect of the downhole small target safety helmet. To this end, the attention mechanism CBAM is fused with YOLOv5s to enhance the feature map of the target area and weaken the background information, so as to help the algorithm better locate the small target helmet. At the same time, a P2 small target detection layer is added on the basis of the original three output layers of YOLOv5s, which increases the multi-scale receptive field of the model and can capture global and local context information at the same time, which improves the detection ability of the algorithm for small targets in complex scenes. In addition, EIoU loss is utilized to replace the original CIoU loss function, solving the problem of fuzzy width-to-height ratio of the prediction frame, ensure the accuracy of the regression frame, and accelerate the convergence speed of the network. By replacing the ordinary convolutional Conv in the YOLOv5s backbone network with ShuffleNetV2, the number of model parameters is greatly reduced and the recognition speed of the model is improved. Finally, the proposed algorithm is compared with YOLOv5s, SSD, Faster-RCNN and YOLOv7 algorithms. The experimental results show that the improved algorithm is applied to the safety helmet detection of mine personnel. Compared with the original YOLOv5s, the accuracy of the proposed algorithm is increased by 2.9%, the recall rate is increased by 2.42%, and the parameter quantity is reduced by 7.6%. Finally, the average accuracy mAP@.5 of the algorithm in the mine safety helmet detection reaches 87.5%.
| [1] |
葛世荣,郝尚清,张世洪,等. 我国智能化采煤技术现状及待突破关键技术[J]. 煤炭科学技术,2020,48(7):28−46.
GE Shirong,HAO Shangqing,ZHANG Shihong,et al. Status of intelligent coal mining technology and potential key technologies in China[J]. Coal Science and Technology,2020,48(7):28−46.
|
| [2] |
王国法,徐亚军,张金虎,等. 煤矿智能化开采新进展[J]. 煤炭科学技术,2021,49(1):1−10.
WANG Guofa,XU Yajun,ZHANG Jinhu,et al. New development of intelligent mining in coal mines[J]. Coal Science and Technology,2021,49(1):1−10.
|
| [3] |
王国法,张良,李首滨,等. 煤矿无人化智能开采系统理论与技术研发进展[J]. 煤炭学报,2023,48(1):34−53.
WANG Guofa,ZHANG Liang,LI Shoubin,et al. Progresses in theory and technological development of unmanned smart mining system[J]. Journal of China Coal Society,2023,48(1):34−53.
|
| [4] |
侯公羽,陈钦煌,杨振华,等. 基于改进YOLOv5的安全帽检测算法[J]. 工程科学学报,2024,46(2):329−342.
HOU Gongyu,CHEN Qinhuang,YANG Zhenhua,et al. Helmet detection method based on improved YOLOv5[J]. Chinese Journal of Engineering,2024,46(2):329−342.
|
| [5] |
杜青,杨仕教,郭钦鹏,等. 地下矿山作业人员佩戴安全帽智能检测方法[J]. 工矿自动化,2023,49(7):134−140.
DU Qing,YANG Shijiao,GUO Qinpeng,et al. Intelligent detection method of working personnel wearing safety helmets in underground mine[J]. Journal of Mine Automation,2023,49(7):134−140.
|
| [6] |
张锦,屈佩琪,孙程,等. 基于改进YOLOv5的安全帽佩戴检测算法[J]. 计算机应用,2022,42(4):1292−1300.
ZHANG Jin,QU Peiqi,SUN Cheng,et al. Safety helmet wearing detection algorithm based on improved YOLOv5[J]. Journal of Computer Applications,2022,42(4):1292−1300.
|
| [7] |
赵红成,田秀霞,杨泽森,等. 改进YOLOv3的复杂施工环境下安全帽佩戴检测算法[J]. 中国安全科学学报,2022,32(5):194−200.
ZHAO Hongcheng,TIAN Xiuxia,YANG Zesen,et al. Safety helmet wearing detection algorithm in complex construction environment based on improved YOLOv3[J]. China Safety Science Journal,2022,32(5):194−200.
|
| [8] |
赵睿,刘辉,刘沛霖,等. 基于改进YOLOv5s的安全帽检测算法[J]. 北京航空航天大学学报,2023,49(8):2050−2061.
ZHAO Rui,LIU Hui,LIU Peilin,et al. Safety helmet detection algorithm based on improved YOLOv5s[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(8):2050−2061.
|
| [9] |
WANG L J,CAO Y Y,WANG S,et al. Investigation into recognition algorithm of helmet violation based on YOLOv5-CBAM-DCN[J]. IEEE Access,2022,10:60622−60632. doi: 10.1109/ACCESS.2022.3180796
|
| [10] |
LIU Y Y,JIANG W R. Detection of wearing safety helmet for workers based on YOLOv4[C]//2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI). Piscataway,NJ:IEEE,2021:83−87.
|
| [11] |
REDMON J,FARHADI A. YOLOv3:an incremental improvement[EB/OL]. (2018−04−08)[2023−05−29]. https://arxiv.org/abs/1804.02767.
|
| [12] |
BOCHKOVSKIY A,WANG C Y,LIAO H. YOLOv4:optimal speed and accuracy of object detection[EB/OL]. (2020−04−23) [2023−05−29]. https://arxiv.org/abs/2004.10934.
|
| [13] |
LI C Y,LI L,JIANG H L,et al. YOLOv6:A single-stage object detection framework for industrial applications[EB/OL]. (2022−09−07) [2023−06−01]. https://arxiv.org/abs/2209.02976.
|
| [14] |
WANG C Y,BOCHKOVSKIY A,LIAO H M. YOLOv7:Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway,NJ:IEEE,2023:7464−7475.
|
| [15] |
Solawetz J,Francesco. What is YOLOv8? The ultimate guide [EB/OL]. (2023−01−11) [2023−06−02]. https://blog.roboflow.com/what-is-yolov8/.
|
| [16] |
REDMON J,DIVVALA S,GIRSHICK R,et al. You only look once:Unified,real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway,NJ:IEEE,2016:779−788.
|
| [17] |
SINGH S K. Multiple fault detection of rolling bearing through ensemble empirical mode decomposition of vibration signal[J]. International Journal of Engineering and Advanced Technology,2019,9(2):2724−2726. doi: 10.35940/ijeat.B3562.129219
|
| [18] |
ZHAO B Y,WU Y F,GUAN X R,et al. An improved aggregated-mosaic method for the sparse object detection of remote sensing imagery[J]. Remote Sensing,2021,13(13):2602. doi: 10.3390/rs13132602
|
| [19] |
苗长云,孙丹丹. 基于改进YOLOv5s的带式输送机滚筒故障检测研究[J]. 工矿自动化,2023,49(7):41−48.
MIAO Changyun,SUN Dandan. Research on fault detection of belt conveyor drum based on improved YOLOv5s[J]. Journal of Mine Automation,2023,49(7):41−48.
|
| [20] |
郝帅,张旭,马旭,等. 基于CBAM-YOLOv5的煤矿输送带异物检测[J]. 煤炭学报,2022,47(11):4147−4156.
HAO Shuai,ZHANG Xu,MA Xu,et al. Foreign object detection in coal mine conveyor belt based on CBAM-YOLOv5[J]. Journal of China Coal Society,2022,47(11):4147−4156.
|
| [21] |
闫志蕊,王宏伟,耿毅德. 基于改进DeeplabV3+和迁移学习的煤岩界面图像识别方法[J]. 煤炭科学技术,2023,51(S1):429−439.
YAN Zhirui,WANG Hongwei,GENG Yide. Coal-rock interface image recognition method based on improved DeeplabV3+ and transfer learning[J]. Coal Science and Technology,2023,51(S1):429−439.
|
| [22] |
付国栋,黄进,杨涛,等. 改进CBAM的轻量级注意力模型[J]. 计算机工程与应用,2021,57(20):150−156. doi: 10.3778/j.issn.1002-8331.2101-0369
FU Guodong,HUANG Jin,YANG Tao,et al. Improved lightweight attention model based on CBAM[J]. Computer Engineering and Applications,2021,57(20):150−156. doi: 10.3778/j.issn.1002-8331.2101-0369
|
| [23] |
鞠默然,罗海波,王仲博,等. 改进的YOLO V3算法及其在小目标检测中的应用[J]. 光学学报,2019,39(7):0715004. doi: 10.3788/AOS201939.0715004
JU Moran,LUO Haibo,WANG Zhongbo,et al. Improved YOLO V3 algorithm and its application in small target detection[J]. Acta Optica Sinica,2019,39(7):0715004. doi: 10.3788/AOS201939.0715004
|
| [24] |
ZHENG Z H,WANG P,LIU W,et al. Distance-IoU loss:Faster and better learning for bounding box regression[J]. Proceedings of the AAAI Conference on Artificial Intelligence,2020,34(7):12993−13000. doi: 10.1609/aaai.v34i07.6999
|
| [25] |
张磊,李熙尉,燕倩如,等. 基于改进YOLOv5s的综采工作面人员检测算法[J]. 中国安全科学学报,2023,33(7):82−89.
ZHANG Lei,LI Xiwei,YAN Qianru,et al. Personnel detection algorithm in fully mechanized coal face based on improved YOLOv5s[J]. China Safety Science Journal,2023,33(7):82−89.
|
| [26] |
陈奎,刘晓,贾立娇,等. 基于轻量化网络与增强多尺度特征融合的绝缘子缺陷检测[J]. 高电压技术,2024,50(3):1289−1300.
CHEN Kui,LIU Xiao,JIA Lijiao,et al. Insulator defect detection based on lightweight network and enhanced multi-scale feature fusion[J]. High Voltage Engineering,2024,50(3):1289−1300.
|
| [27] |
邵小强,李鑫,杨涛,等. 改进YOLOv5s和DeepSORT的井下人员检测及跟踪算法[J]. 煤炭科学技术,2023,51(10):291−301.
SHAO Xiaoqiang,LI Xin,YANG Tao,et al. Underground personnel detection and tracking based on improved YOLOv5s and DeepSORT[J]. Coal Science and Technology,2023,51(10):291−301.
|
| [28] |
LIU W,ANGUELOV D,ERHAN D,et al. SSD:Single shot MultiBox detector[C]// Computer Vision−ECCV 2016. Cham:Springer International Publishing,2016:21−37.
|
| [29] |
REN S Q,HE K M,GIRSHICK R,et al. Faster R-CNN:Towards real-time object detection with region proposal networks[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence. Piscataway,NJ:IEEE,2017:1137−1149.
|
| [1] | MAO Qinghua, HU Xin, WANG Menghan, ZHANG Xuhui, XUE Xusheng. Interference state intelligent recognition method for shearer drum and hydraulic support guard plate of improved YOLOv5s algorithm[J]. COAL SCIENCE AND TECHNOLOGY, 2024, 52(2): 253-263. DOI: 10.12438/cst.2023-0757 |
| [2] | SHAO Xiaoqiang, LI Xin, YANG Tao, YANG Yongde, LIU Shibo, YUAN Zewen. Underground personnel detection and tracking based on improved YOLOv5s and DeepSORT[J]. COAL SCIENCE AND TECHNOLOGY, 2023, 51(10): 291-301. DOI: 10.13199/j.cnki.cst.2022-1933 |
| [3] | DI Junzhen, ZHANG Siyi, YANG Yu, LIANG Bing, WANG Xianjun, MENG Fankang, LI Zengxin, GUO Jianzhi, LI Tuoda, ZHANG Wei. Study on adsorption characteristics of modified lignite for Fe2+, Mn2+ in acid mine drainage[J]. COAL SCIENCE AND TECHNOLOGY, 2022, 50(11): 243-250. |
| [4] | DI Junzhen, LI Mingwei, WANG Xianjun, YANG Yu, LIANG Bing, MENG Fankang, ZHOU Xinhua, KONG Tao, LI Zengxin. Experimental study on adsorption of Cu2+ and Zn2+ in acid mine wastewater by modified lignite[J]. COAL SCIENCE AND TECHNOLOGY, 2022, 50(3): 301-307. |
| [5] | SONG Dongping ZHOU Xihua LI Jingyang BAI Gang, . Liquid carbon dioxide phase-change refrigeration and cooling technology of high temperature mine[J]. COAL SCIENCE AND TECHNOLOGY, 2017, (10). |
| [6] | ZHANG Ping-song HU Xiong-wu, . Research status on technology of advanced detection by electromagnetic methods in mine laneway[J]. COAL SCIENCE AND TECHNOLOGY, 2015, (1). |
| [7] | Development on Distributed Mine Temperature and Humidity Monitoring and Measuring System Based on CAN Bus[J]. COAL SCIENCE AND TECHNOLOGY, 2011, (9). |
| [8] | Study on Na2S Deoxidation of Coal Bed Methane in Low Gassy Mine[J]. COAL SCIENCE AND TECHNOLOGY, 2011, (7). |
| [9] | Improved Shortest Path Algorithm Based on Mine Geographic Network Model[J]. COAL SCIENCE AND TECHNOLOGY, 2011, (2). |
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: