基于深度学习的地铁施工作业人员不安全行为识别与应用

doi:10.16265/j.cnki.issn1003-3033.2023.01.0874

中国安全科学学报 ›› 2023, Vol. 33 ›› Issue (1): 41-47.doi: 10.16265/j.cnki.issn1003-3033.2023.01.0874

• 安全社会科学与安全管理 • 上一篇下一篇

基于深度学习的地铁施工作业人员不安全行为识别与应用

范冰倩¹(), 董秉聿¹, 王彪¹, 李铭¹, 吴松², 佟瑞鹏¹^,^**()

¹ 中国矿业大学(北京) 应急管理与安全工程学院,北京 10008
² 中国职业安全健康协会,北京 100029

收稿日期:2022-08-10 修回日期:2022-11-15 出版日期:2023-01-28 发布日期:2023-07-28
通讯作者: ** 佟瑞鹏(1977—),男,黑龙江穆棱人,博士,教授,主要从事行为安全管理、职业心理健康、环境风险评估等方面的研究。E-mail: tongrp@cumtb.edu.cn。
作者简介:
范冰倩 (1990—),女,陕西渭南人,博士研究生,主要研究方向为行为安全管理。E-mail: fanbingqian@126.com。
吴松,工程师
基金资助:
北京市自然科学基金资助(8212015); 国家自然科学基金资助(52074302)

Identification and application of unsafe behaviors of subway construction workers based on deep learning

FAN Bingqian¹(), DONG Bingyu¹, WANG Biao¹, LI Ming¹, WU Song², TONG Ruipeng¹^,^**()

¹ School of Emergency Management and Safety Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
² China Occupational Safety and Health Association, Beijing 100029, China

Received:2022-08-10 Revised:2022-11-15 Online:2023-01-28 Published:2023-07-28

摘要/Abstract

摘要：

为有效识别地铁施工作业人员不安全行为,基于深度学习与计算机视觉技术,提出融合行为和身份识别的不安全行为识别方法。首先,对更快速的基于区域的卷积神经网络(Faster R-CNN)算法进行优化,引入高效通道注意力(ECA)模块提升行为识别的准确性;其次,将基于人脸超分辨率算法的人脸识别方法与行为识别相结合,提升图像像素水平并准确输出不安全行为执行人员相关信息;然后,行为识别与人脸识别并发进行,识别结果回流至数据库最终输出工人不安全行为报告;最后,选取某地铁施工项目的4种不安全行为进行识别方法的实证应用。研究表明:该方法可在地铁施工场景下进行有效应用,不安全行为识别和执行人员身份识别的准确率均达0.85以上,具有较高的准确度。

关键词: 深度学习, 地铁施工, 不安全行为识别, 作业人员, 更快速的基于区域的卷积神经网络(Faster R-CNN), 人脸识别

Abstract:

In order to effectively identify unsafe behaviors of subway construction workers, an unsafe behavior recognition method combining behavior and identity recognition was proposed based on deep learning and computer vision technology. Firstly, it is optimized the existing Faster R-CNN algorithm, and the ECA attention module is introduced to improve the accuracy of behavior recognition. Secondly, the face recognition method based on the face super-resolution algorithm is combined with behavior recognition to improve the pixel level of the image and accurately output the relevant information of the perpetrators of unsafe behavior. Thirdly, behavior recognition is performed concurrently with face recognition, and the results are streamed back to the database for the final output of the worker's unsafe behavior report. Finally, four unsafe behaviors from a subway construction project were selected for the empirical application of the recognition method. The research shows that the method can be effectively applied in subway construction scenarios, and the accuracy of both behavior recognition and face recognition reached above 0.85, which has high accuracy.

Key words: subway construction, unsafe behavior recognition, construction workers, faster region-based convolutional neural networks(Faster R-CNN), face recognition

范冰倩, 董秉聿, 王彪, 李铭, 吴松, 佟瑞鹏. 基于深度学习的地铁施工作业人员不安全行为识别与应用[J]. 中国安全科学学报, 2023, 33(1): 41-47.

FAN Bingqian, DONG Bingyu, WANG Biao, LI Ming, WU Song, TONG Ruipeng. Identification and application of unsafe behaviors of subway construction workers based on deep learning[J]. China Safety Science Journal, 2023, 33(1): 41-47.

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参考文献 18

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标签类别	数量	比例%
安全帽	2 360	29.01
无安全帽	2 828	34.76
翻越护栏	988	12.14
安全带	719	8.84
无安全带	1 241	15.25
合计	8 136	100

各标签类别AP					MAP
佩戴安全帽	未佩戴安全帽	翻越护栏	系安全带	未系安全带	MAP
0.93	0.91	0.72	0.88	0.82	0.854

基于深度学习的地铁施工作业人员不安全行为识别与应用

Identification and application of unsafe behaviors of subway construction workers based on deep learning

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