建筑业事故多发,建筑工人的不安全行为已被确定为事故的主要原因。预防事故的一个重要对策是监测和管理这些不安全行为。检测和识别工人不安全行为的最常用方法是基于计算机视觉的智能监控系统。然而,大多数现有的研究或产品只关注工人的行为(即,运动)识别,有限的研究考虑了人-机之间的相互作用,人的物质或人的环境。这些相互作用对于判断工人的行为是否安全非常重要,从安全管理的角度来看。本研究旨在开发一种识别建筑工人不安全行为的新方法,即,人-机/材料之间的不安全相互作用,基于ST-GCN(时空图卷积网络)和YOLO(你只看一次),为安全管理提供更直接、更有价值的信息。在这项研究中,两个经过训练的基于YOLO的模型是,分别,用于检测工作场所的安全标志,和与工人互动的物体。然后,对ST-GCN模型进行了训练,以检测和识别工人的行为。最后,考虑到人-机/材料之间的相互作用,开发了一种决策算法,基于YOLO和ST-GCN结果。结果表明,所开发的方法具有良好的性能,与仅使用ST-GCN相比,精度从51.79%显著提高到85.71%,61.61%到99.11%,和58.04%至100.00%,分别,在识别以下三种行为时,投掷(投掷锤子,投掷瓶子),操作(打开开关,放瓶子),和穿越(穿越栏杆和穿越障碍物)。研究结果对安全管理具有一定的现实意义,特别是工人的行为监控和管理。
The construction industry is accident-prone, and unsafe behaviors of construction workers have been identified as a leading cause of accidents. One important countermeasure to prevent accidents is monitoring and managing those unsafe behaviors. The most popular way of detecting and identifying workers\' unsafe behaviors is the computer vision-based intelligent monitoring system. However, most of the existing research or products focused only on the workers\' behaviors (i.e., motions) recognition, limited studies considered the interaction between man-machine, man-material or man-environments. Those interactions are very important for judging whether the workers\' behaviors are safe or not, from the standpoint of safety management. This study aims to develop a new method of identifying construction workers\' unsafe behaviors, i.e., unsafe interaction between man-machine/material, based on ST-GCN (Spatial Temporal Graph Convolutional Networks) and YOLO (You Only Look Once), which could provide more direct and valuable information for safety management. In this study, two trained YOLO-based models were, respectively, used to detect safety signs in the workplace, and objects that interacted with workers. Then, an ST-GCN model was trained to detect and identify workers\' behaviors. Lastly, a decision algorithm was developed considering interactions between man-machine/material, based on YOLO and ST-GCN results. Results show good performance of the developed method, compared to only using ST-GCN, the accuracy was significantly improved from 51.79% to 85.71%, 61.61% to 99.11%, and 58.04% to 100.00%, respectively, in the identification of the following three kinds of behaviors, throwing (throwing hammer, throwing bottle), operating (turning on switch, putting bottle), and crossing (crossing railing and crossing obstacle). The findings of the study have some practical implications for safety management, especially workers\' behavior monitoring and management.