|本期目录/Table of Contents|

[1]崔铁军,郭大龙.基于改进YOLOX的变电站工人防护设备检测研究*[J].中国安全生产科学技术,2023,19(4):201-206.[doi:10.11731/j.issn.1673-193x.2023.04.029]
　CUI Tiejun,GUO Dalong.Research on detection of protection equipment for substation workers based on improved YOLOX[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2023,19(4):201-206.[doi:10.11731/j.issn.1673-193x.2023.04.029]
点击复制

基于改进YOLOX的变电站工人防护设备检测研究*
分享到：

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
19
期数:
2023年4期
页码:
201-206
栏目:
职业安全卫生管理与技术
出版日期:
2023-04-30

文章信息/Info

Title:
Research on detection of protection equipment for substation workers based on improved YOLOX
文章编号:
1673-193X(2023)-04-0201-06
作者:
崔铁军; 郭大龙
（辽宁工程技术大学 安全科学与工程学院，辽宁 葫芦岛 125105）
Author(s):
CUI Tiejun; GUO Dalong
(College of Safety Science and Engineering,Liaoning Technical University,Huludao Liaoning 125105,China)
关键词:
电气安全; 改进YOLOX; 变电站; 工人防护; 防护设备检测; 注意力机制
Keywords:
electrical safety;  improved YOLOX (you only look once X);  substation;  worker protection;  electrical workers’ protective equipment detection;  attention mechanism
分类号:
X913.4
DOI:
10.11731/j.issn.1673-193x.2023.04.029
文献标志码:
A
摘要:
为解决电气工人防护设备检测问题，通过改进YOLOX算法，提出检测工作人员防护设备的模型。首先在预测部分改进损失函数，为解决损失函数计算存在的缺陷，对IOU损失的计算方法进行改进，根据防护设备任务特性，通过调整各种类型损失函数的权重，增加对模型误判的惩罚，对模型进行优化；其次在算法主干网络中引入CBAM注意力模块提高神经网络对工人防护设备的感知能力；最后在算法Neck部分，将UpSample结构用于多尺度特征融合，加强网络的细节表达能力，从而提升对小目标困难样本的检测精度。研究结果表明：改进后的YOLOX模型平均精度均值达到87.24%，与已有YOLOX模型相比提升2.46%，具备有效性，适用于变电站工人防护设备检测。研究结果可为电气工人提供更高的防护装备检测精度。
Abstract:
Aiming at the detection problem of electrical workers’ protective equipment,a model to detect the workers’ protective equipment was proposed by improving YOLOX (you only look once X) algorithm.Firstly,the loss function was improved in the prediction part,and the calculation method of intersection over union (IOU) loss was improved in order to solve the disadvantages of loss function calculation.At the same time,the model was optimized by adjusting the weights of various types of loss functions and increasing the penalties for model misjudgment according to the task characteristics of protective equipment.Secondly,the convolutional block attention module (CBAM) was introduced into algorithm backbone network to enhance the perception capability of neural network on workers’ protective equipment.Finally,the UpSample structure was introduced into the Neck part of the algorithm for multi-scale feature fusion to enhance the expression performance of network details,thus improve the recognition accuracy of small targets.The results showed that the mean average precision of the experiment on the data set reached up to as high as 87.24% by using the improved YOLOX model,which was 2.46% higher than the existing YOLOX model.It was effective and suitable for the detection of substation workers’ protective equipment.The research results can provide higher detection accuracy of protective equipment for electrical workers.

参考文献/References:

［1］ALOM M Z,TAHA T M,YAKOPCIC C,et al.The history began from AlexNet:a comprehensive survey on deep learning approaches［EB/OL］.(2018-09-12)［2022-06-12］.https://arxiv.org/abs/1803.01164v2.
［2］WANG L,GUO S,HUANG W,et al.Places205-VGGNet models for scene recognition［EB/OL］.(2015-08-07)［2022-06-07］.https://arxiv.org/abs/1508.01667.
［3］BALLESTE R P,ARAUJOR M.On the performance of GoogleNet and AlexNet applied to sketches［C］//Thirtieth AAAI Conference on Artificial Intelligence.Phoenix:AAAI Press,2016.
［4］HAQUE M F,LIM H Y,KANG D S.Object detection based on VGG with ResNet network［C］//2019 International Conference on Electronics,Information,and Communication (ICEIC).Auckland:IEEE,2019:1-3.
［5］RUBAIYAT A H M,TOMA T T,KALANTARI-KHANDANI M,et al.Automatic detection of helmet uses for construction safety［C］//2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW).ACM,2016.
［6］LI J,LIU H,WANG T,et al.Safety helmet wearing detection based on image processing and machine learning［C］//2017 Ninth International Conference on Advanced Computational Intelligence (ICACI).Omaha,NE:IEEE,2017.
［7］张玉涛,张梦凡,史学强,等.人员安全帽佩戴轻量化检测方法研究［J］.安全与环境学报,2023,23(2):474-480. ZHANG Yutao,ZHANG Mengfan,SHI Xueqiang,et al.Research on lightweight detection methods for personnel helmet wearing［J］.Journal of Safety and Environment,2023,23(2):474-480.
［8］毕林,谢伟,崔君.基于卷积神经网络的矿工安全帽佩戴识别研究［J］.黄金科学技术,2017,25(4):73-80. BI Lin,XIE Wei,CUI Jun.Identification research on the miner’s safety helmet wear based on convolutional neural network［J］.Gold Science and Technology,2017,25(4):73-80.
［9］GIRSHICK R.Fast R-CNN［C］//Proceedings of the IEEE International Conference on Computer Vision.Santiago:IEEE,2015:1440-1448.
［10］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.
［11］REDMON J,DIVVALA S,GIRSHICK R，et al.You only look once:unified，real-time object detection［C］//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Las Vegas,NV:IEEE,2016:779-788.
［12］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.
［13］赵丽宏,付雪,肖博宇,等.基于卷积神经网络的森林火灾烟雾探测算法研究［J］.中国新技术新产品,2019(12):23-24.
［14］张永翔,吴功平,刘中云,等.基于YOLOv3网络的输电线路防震锤和线夹检测迁移学习［J］.计算机应用,2020,40(S2):188-194. ZHANG Yongxiang,WU Gongping,LIU Zhongyun,et al.Transfer learning of transmission line damper and clamp detection based on YOLOv3 network［J］.Journal of Computer Applications,2020,40(S2):188-194.
［15］REDMON J,FARHADI A.YOLOv3:an incremental improvement［EB/OL］.(2018-04-08)［2022-06-08］.https://arxiv.org/abs/1804.02767.
［16］谢逸,张竞文,李韬,等.基于视频监控的地铁施工不安全行为检测预警［J］.华中科技大学学报(自然科学版),2019,47(10):46-51. XIE Yi,ZHANG Jingwen,LI Tao,et al.Research on detection and early warning of unsafe behavior in metro construction based on video monitoring［J］.Journal of Huazhong University of Science and Technology (Natural Science Edition),2019,47(10):46-51.
［17］王雨生,顾玉宛,封晓晨,等.基于姿态估计的安全帽佩戴检测方法研究［J］.计算机应用研究,2021,38(3):937-940,945. WANG Yusheng,GU Yuwan,FENG Xiaochen,et al.Research on detection method of helmet wearing based on attitude estimatlon［J］.Application Research of Computers,2021,38(3):937-940,945.
［18］李华,王岩彬,益朋,等.基于深度学习的复杂作业场景下安全帽识别研究［J］.中国安全生产科学技术,2021,17(1):175-181. LI Hua,WANG Yanbin,YI Peng,et al.Research on recognition of safety helmets under complex operation scenes based on deep learning［J］.Journal of Safety Science and Technology,2021,17(1):175-181.
［19］屈文谦,邱志斌,廖才波,等.基于YOLOv3的电网作业人员安全帽佩戴检测［J］.中国安全生产科学技术,2022,18(2):214-219. QU Wenqian,QIU Zhibin,LIAO Caibo,et al.Detection on safety helmet wearing of power grid operators based on YOLOv3［J］.Journal of Safety Science and Technology,2022,18(2):214-219.
［20］CAO Zhe,SIMON T,WEI S E,et al.Realtime multi-person 2D pose estimation using part affinity fields［C］//IEEE Transactions on Pattern Analysis and Machine Intelligence.Honolulu,HI:IEEE,2017.
［21］刘浩,刘海滨,孙宇,等.煤矿井下员工不安全行为智能识别系统［J］.煤炭学报,2021,46(S2):1159-1169. LIU Hao,LIU Haibin,SUN Yu,et al.Intelligent recognition system of unsafe behavior of underground coal miners［J］.Journal of China Coal Society,2021,46(S2):1159-1169.
［22］崔铁军,王凌霄.YOLOv4目标检测算法在煤矿工人口罩佩戴监测工作中的应用研究［J］.中国安全生产科学技术,2021,17(10):66-71. CUI Tiejun,WANG Lingxiao.Research on application of YOLOv4 object detection algorithm in monitoring on masks wearing of coal miners［J］.Journal of Safety Science and Technology,2021,17(10):66-71.
［23］FANG Q,LI H,LUO X,et al.Detecting non-hardhat-use by a deep learning method from far-field surveillance videos［J］.Automation in Construction,2018,85:1-9.
［24］GE Z,LIU S T,WANG F,et al.YOLOX:exceeding YOLO series in 2021［EB/OL］.(2021-08-06)［2022-06-06］.https://arxiv.org/pdf/2107.08430.pdf.
［25］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.Seattle,WA:IEEE,2020:390-391.
［26］WOO S,PARK J,LEE J Y,et al.CBAM:convolutional block attention module［C］//Proceedings of the European Conference on Computer Vision (ECCV).Springer,2018:3-19.

相似文献/References:

[1]钮英建,杨玲.北京地区村镇建筑防火现状调查与分析[J].中国安全生产科学技术,2011,7(10):138.
　NIU Ying-jian,YANG Ling.Investigation and analysis on current situation of fire protection?in rural areas of Beijing[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2011,7(4):138.
[2]刘松海.基于人因失误分析的医院电气安全管理研究[J].中国安全生产科学技术,2013,9(5):185.[doi:10.11731/j.issn.1673-193x.2013.05.036]
　LIU Song hai.Research of electrical safety management in hospital based on human error analysis[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2013,9(4):185.[doi:10.11731/j.issn.1673-193x.2013.05.036]
[3]李麟,汪俊文,余纬.数控机床电气安全检测技术探讨[J].中国安全生产科学技术,2015,11(2):166.[doi:10.11731/j.issn.1673-193x.2015.02.027]
　LI Lin,WANG Jun-wen,YU Wei.Discussion on testing technology for electrical safety of NC machine[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2015,11(4):166.[doi:10.11731/j.issn.1673-193x.2015.02.027]
[4]高彦,宋立冬,杜一锋,等.基于PLM的矿用低压变频器漏电机理的研究[J].中国安全生产科学技术,2016,12(10):130.[doi:10.11731/j.issn.1673-193x.2016.10.022]
　GAO Yan,SONG Lidong,DU Yifeng,et al.Research on electric leakage mechanism of low voltagefrequency converter for mining based on PLM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2016,12(4):130.[doi:10.11731/j.issn.1673-193x.2016.10.022]

备注/Memo

备注/Memo:
收稿日期： 2022-07-03
* 基金项目： 国家自然科学基金项目(52004120)；国家重点研发计划项目(2017YFC1503102)；辽宁省教育厅项目(LJ2020QNL018)；辽宁工程技术大学学科创新团队项目(LNTU20TD-31)
作者简介： 崔铁军，博士，副教授，主要研究方向为系统安全及智能分析理论。
通信作者： 郭大龙，硕士研究生，主要研究方向为系统安全及深度学习。
更新日期/Last Update: 2023-05-11