|本期目录/Table of Contents|

[1]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.[doi:10.11731/j.issn.1673-193x.2021.10.010]
　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.[doi:10.11731/j.issn.1673-193x.2021.10.010]
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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*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
17
期数:
2021年10期
页码:
66-71
栏目:
职业安全卫生管理与技术
出版日期:
2021-10-31

文章信息/Info

Title:
Research on application of YOLOv4 object detection algorithm in monitoring on masks wearing of coal miners
文章编号:
1673-193X(2021)-10-0066-06
作者:
崔铁军; 王凌霄
（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;  WANG Lingxiao
(College of Safety Science and Engineering,Liaoning Technical University,Huludao Liaoning 125105,China)
关键词:
目标检测; 口罩检测; 深度学习; 人脸识别; 职业安全卫生
Keywords:
object detection;  mask detection;  deep learning;  face recognition;  occupational safety and health
分类号:
X961
DOI:
10.11731/j.issn.1673-193x.2021.10.010
文献标志码:
A
摘要:
为防止煤矿工人吸入过量粉尘而导致职业性尘肺病，基于Keras框架利用YOLOv4 (you only look once)目标检测算法对井下人员佩戴防尘口罩情况进行高精度且快速的检测与识别，并与MTCNN(Multi-task convolutional neural network)和FaceNet构成的人脸识别算法相结合，进行煤矿工人口罩佩戴监测的研究。结果表明：模型对井下人员口罩佩戴有较高的检测精度，识别已佩戴口罩的矿井下作业人员的平均精度达到92.78%，识别未佩戴防尘口罩检测的平均精度为91.63%，与其他主流算法相比算法具有更好的鲁棒性和检测效果。研究结果为预防煤矿工人职业性尘肺病提供1种有效的技术手段。
Abstract:
In order to prevent the coal miners from inhaling excessive dust and causing occupational pneumoconiosis,based on the Keras framework,the YOLOv4 (you only look once) object detection algorithm was used to detect and identify the situation of underground workers wearing dust-proof masks accurately and rapidly.Combined with the face recognition algorithm composed of MTCNN (Multi-task convolutional neural network) and FaceNet,the research on the monitoring of coal miners’ mask wearing was carried out.The results showed that the model had a high detection accuracy for the masks wearing of underground workers.The average accuracy for the recognition on the masks wearing of underground miners was 92.78%,and the average accuracy for the recognition on without wearing dust-proof masks was 91.63%,so the algorithm had better robustness and detection effect compared with other mainstream algorithms.The model provides a way and method for the coal miners to prevent the occupational pneumoconiosis,and its effectiveness was verified by experiments.

参考文献/References:

［1］SILVER D,HUANG A,MADDISON C,et al.Mastering the game of go with deep neural networks and tree search［J］.Nature,2016,529 (7587);484-489.
［2］牛作东,覃涛,李捍东,等.改进Retina Face的自然场景口罩佩戴检测算法［J］.计算机工程与应用,2020,56(12):1-7. NIU Zuodong,QIN Tao,LI Handong,et al.Improved algorithm of Retina Face for natural scene mask wear detection［J］.Computer Engineering and Applications,2020,56(12):1-7.
［3］张修宝,林子原,田万鑫,等.全天候自然场景下的人脸佩戴口罩识别技术［J］.中国科学:信息科学,2020,50(7):1110-1120. ZHANG Xiubao,LIN Ziyuan,TIAN Wanxin,et al.Mask-wearing recognition in the wild［J］.Scientia Sinica(Informationis),2020,50(7):1110-1120.
［4］曹城硕,袁杰.基于YOLO-Mask算法的口罩佩戴检测方法［J/OL］.激光与光电子学进展:1-13［2020-11-20］.http://kns.cnki.net/kcms/detail/31.1690.TN.20201009.1330.006.html. CAO Chengshuo,YUAN Jie.Mask wearing detection method based on YOLO-Mask［J/OL］.Laser & Optoelectronics Progress:1-13［2020-11-20］.
［5］魏丽,王洁,姜昕言,等.遮挡条件下的人脸检测与遮挡物属性判识［J］.计算机仿真,2020,37(9):441-445，450. WEI Li,WANG Jie,JIANG Xinyan,et al.Face detection and obstacle attribute identification under occlusion［J］.Computer Simulation,2020,37(9):441-445,450.
［6］王艺皓,丁洪伟,李波,等.复杂场景下基于改进YOLOv3的口罩佩戴检测算法［J］.计算机工程,2020,46(11):12-22. WANG Yihao,DING Hongwei,LI Bo,et al.Mask wearing detection algorithm based on improved YOLOv3 in complex scenes［J］.Computer Engineering,2020,46(11):12-22.
［7］赵江平,王垚.基于图像识别技术的不安全行为识别［J］.安全与环境工程,2020,27(1):158-165. ZHAO Jiangping,WANG Yao.Unsafe behavior recognition based on image recognition technology［J］.Safety and Environmental Engineering,2020,27(1):158-165.
［8］佟瑞鹏,张艳伟.人工智能技术在矿工不安全行为识别中的融合应用［J］.中国安全科学学报,2019,29(1):7-12. TONG Ruipeng,ZHANG Yanwei.Integration between artificial intelligence technologies for miners’ unsafe behavior identification［J］.China Safety Science Journal,2019,29(1):7-12.
［9］施辉,陈先桥,杨英.改进YOLO v3的安全帽佩戴检测方法［J］.计算机工程与应用,2019,55(11):213-220. SHI Hui,CHEN Xianqiao,YANG Ying.Safety helmet wearing detection method of improved YOLO v3［J］.Computer Engineering and Applications,2019,55(11):213-220.
［10］车畅畅,王华伟,倪晓梅,等.基于深度学习的航空发动机故障融合诊断［J］.北京航空航天大学学报,2018,44(3):621-628. CHE Changchang WANG Huawei，NI Xiaomei,et al.Fault fusion diagnosis of aero-engine based on deep learning［J］.Journal of Beijing University of Aeronautics and Astronautics,2018,44(3):621-628.
［11］康高强,高仕斌,于龙,等.基于深度学习的高铁接触网旋转双耳开口销钉缺失故障检测［J］.铁道学报,2020,42(10):45-51. KANG Gaoqiang,GAO Shibin,YU Long,et al.Fault detection of missing split pins in swivel with clevis in high-speed railway catenary based on deep learning［J］.Journal of the China Railway Society,2020,42(10):45-51.
［12］周双林,杨星,刘克中,等.规则约束下基于深度强化学习的船舶避碰方法［J］.中国航海,2020,43(3):27-32，46. ZHOU Shuanglin,YANG Xing,LIU Kezhong,et al.Colregs-compliant method for ship collision avoidance based on deep reinforcement learning［J］.Navigation of China,2020,43(3):27-32,46.
［13］李千登,王廷春,崔靖文,等.基于深度学习算法的叉车危险操作行为检测［J］.中国安全生产科学技术,2020,16(5):155-159. LI Qiandeng,WANG Tingchun,CUI Jingwen,et al.Detection on dangerous operation behavior of forklift based on deep learning algorithm［J］.Journal of Safety Science and Technology,2020,16(5):155-159.
［14］BOCHKOVSKIY A,WANG C Y,LIAO H Y M.Yolov4:optimal speed and accuracy of object detection［EB/OL］.https://arxiv.org/abs/2004.10934，2020-04-23.
［15］WANG C Y,LIAO H Y M,WU Y H,et al.CSPNet:a new backbone that can enhance learning capability of CNN［EB/OL］.https://arxiv.org/abs/1911.11929，2019-11-27.
［16］REN S Q,HE K M,GIRSHIC K R,et al.Faster r-cnn:towards real-time object detection with region proposal networks［J］.Advances in Neural Information Processing Systems,2015,28:91-99.
［17］LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot multiBox detector［EB/OL］.https://link.springer.com/chapter/10.1007/978-3-319-46448-0_2，2016-09-17.
［18］RDMONE J,FARHADI A.YOLOv3:an incremental improvement［EB/OL］.https://arxiv.org/abs/1804.02767，2018-04-08.

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备注/Memo

备注/Memo:
收稿日期： 2020-12-03
* 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： 王凌霄，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: 2021-11-03