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

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。

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.