ST-GCN在建筑工人不安全动作识别中的应用  

Application of ST-GCN in unsafe action identification of construction workers

作　　者：刘耀 焦双健[1] LIU Yao;JIAO Shuangjian(College of Engineering,Ocean University of China,Qingdao Shandong 266100,China)

机构地区：[1]中国海洋大学工程学院,山东青岛266100

出　　处：《中国安全科学学报》2022年第4期30-35,共6页China Safety Science Journal

摘　　要：为准确及时地识别施工现场工人的不安全动作,运用计算机视觉的方法,结合图像识别技术和建筑安全知识,提出一种基于骨架的实时识别方法。将姿态估计算法与动作识别算法结合搭建组合模型,通过全面的数据进行模型训练,进而实现动作分类和不安全动作的识别,其中,AlphaPose用于姿态估计提取骨骼关键点坐标位置,时空图卷积网络(ST-GCN)用于动作识别,并通过试验进行验证。结果表明:该方法识别爬梯危险动作的准确率可以达到98.48%,同时,ST-GCN与支持向量机、随机森林、长短期记忆网络(LSTM)相比,具有更强的泛化能力。该方法通过对现场工人的不安全动作进行实时识别和预警,可改善传统安全管理模式,提高安全管理信息化水平。In order to identify unsafe actions of construction workers accurately and timely,a real-time recognition method based on skeleton was proposed by using computer vision,image recognition technology and safety knowledge of buildings.Then,pose estimation algorithm and action recognition algorithm were combined to establish a model,and motion classification and unsafe motion recognition were realized by training the model with comprehensive data.Among them,AlphaPose was used for posture estimation to extract coordinate positions of key points of bones,and ST-GCN was adopted for action recognition.Finally,the model was verified through experiment.The results show that the method's accuracy rate of dangerous actions identification for ladder climbing can reach 98.48%.Meanwhile,ST-GCN has stronger generalization ability compared with support vector machine,random forest and LSTM.This method can improve traditional safety management mode and information application of safety management through real-time identification and early warning of unsafe actions of workers on site.

关 键 词：时空图卷积网络(ST-GCN) 建筑工人 不安全行为 动作识别 AlphaPose 长短期记忆网络(LSTM) 

分 类 号：X912.9[环境科学与工程—安全科学]