|本期目录/Table of Contents|

[1]李立功,刘晓,赵旭.基于深度学习的地下工程施工人员不安全行为识别研究*[J].中国安全生产科学技术,2025,21(5):55-62.[doi:10.11731/j.issn.1673-193x.2025.05.007]
　LI Ligong,LIU Xiao,ZHAO Xu.Research on unsafe behavior identification of construction workers in underground engineering based on deep learning[J].Journal of Safety Science and Technology,2025,21(5):55-62.[doi:10.11731/j.issn.1673-193x.2025.05.007]
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基于深度学习的地下工程施工人员不安全行为识别研究*

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

卷:
21
期数:
2025年5期
页码:
55-62
栏目:
学术论著
出版日期:
2025-05-30

文章信息/Info

Title:
Research on unsafe behavior identification of construction workers in underground engineering based on deep learning
文章编号:
1673-193X(2025)-05-0055-08
作者:
李立功; 刘晓; 赵旭
（1.太原理工大学 经济与管理学院，山西 太原 030024；
2.中国矿业大学（北京） 应急管理与安全工程学院，北京 100083）
Author(s):
LI Ligong;  LIU Xiao;  ZHAO Xu
(1.College of Economics and Management,Taiyuan University of Technology,Taiyuan Shanxi 030024,China;
2.School of Emergency Management and Safety Engineering,China University of Mining & Technology-Beijing,Beijing 100083,China)
关键词:
深度学习; 地下工程; 不安全行为; 智能识别
Keywords:
deep learning;  underground engineering;  unsafe behavior;  intelligent identification
分类号:
X947
DOI:
10.11731/j.issn.1673-193x.2025.05.007
文献标志码:
A
摘要:
为更及时有效地识别地下工程施工人员不安全行为，弥补现有智能识别方法在不安全行为及行为发出人员等耦合信息理解方面的不足，基于深度学习、计算机视觉技术，提出1种融合身份识别与姿态识别的不安全行为识别方法。该方法结合地下工程施工现场的低照度、易遮挡特征，在模型中强化图像的预处理，引入YOLO系列、双流SlowFast和ArcFace等模型，并对损失函数等进行改进。研究结果表明：改进的身份识别和姿态识别模型的平均精度均有不同幅度的提升，对于安全帽遮挡环境和脸部灰尘遮挡环境，身份识别模型的平均精度分别提升了7.03％和8.34％，对于复杂环境的应用表现良好。研究结果可为地下工程施工人员的不安全行为精准识别与监测预警提供参考。
Abstract:
To identify the unsafe behavior of construction workers in underground engineering more timely and effectively,and compensate for the shortcomings of existing intelligent identification methods in understanding the coupled information such as unsafe behavior and behavior emitting personnel,an identification method of unsafe behavior integrating the identity recognition and posture recognition was proposed based on the deep learning and computer vision technology.Combining with the low illumination and easy occlusion characteristics of underground construction sites,the image preprocessing was strengthened in the model,then the YOLO series,dual flow SlowFast,ArcFace and other models were introduced,and the loss function was improved.The results show that both the average accuracies of the improved identity recognition and posture recognition models are improved to varying degrees.For the environments with safety helmets and facial dust obstruction,the average accuracy of the identity recognition model is improved by 7.03% and 8.34%,respectively,presenting good performance in complex environments.The research results can provide reference for the accurate identification and monitoring and early warning on the unsafe behavior of construction workers in underground engineering.

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备注/Memo

备注/Memo:
收稿日期： 2025-03-02
* 基金项目： 山西省应用基础研究计划项目（20210302124440）
作者简介： 李立功，博士，讲师，主要研究方向为行为安全、煤矿安全与开采。
通信作者： 赵旭，博士研究生，主要研究方向为行为安全、安全管理。
更新日期/Last Update: 2025-05-26