工人不安全行为智能识别与矫正研究  

Artificial intelligence-based recognition and modification of workers’ unsafe behavior

作　　者：方伟立 丁烈云[1] FANG Weili;DING Lieyun(Schoo of Civil and Hydraulic Engineering,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]华中科技大学土木与水利工程学院,湖北武汉430074

出　　处：《华中科技大学学报（自然科学版）》2022年第8期131-135,共5页Journal of Huazhong University of Science and Technology(Natural Science Edition)

基　　金：国家自然科学基金资助项目(71732001,51978302,U21A20151)。

摘　　要：针对当前基于行为安全(BBS)的研究存在不安全行为数据未充分利用、分析能力不足、矫正缺乏针对性等问题,构建了基于BBS和机器视觉的工人不安全行为智能识别与矫正框架体系.该框架体系主要包括不安全行为智能识别、行为安全个性化培训和不安全行为智能预测,并被应用于武汉两湖隧道工程.研究结果表明:该框架系统从基于场景的不安全行为自动化识别、基于自适应学习的行为安全个性化培训和基于现场工人运动轨迹的不安全行为预测三个方面,实现了对工人不安全行为的“事中-事后-事前”管理,为探索机器视觉技术支持的行为安全管理提供了新思路.The existing research on behaviour-based safety(BBS) research has the following challenges,unsafe behaviour data is not fully utilised, unsafe behaviour analysis ability is insufficient, and unsafe behaviour correction lacks pertinence. Against this contextual backdrop,a framework system for intelligent recognition and correction of workers’ unsafe behaviors based on BBS and machine vision was constructed. The proposed framework consists of automated identification of workers’ unsafe behaviour,personalised safety training for behaviour modification, and automated prediction and tracking of workers’ unsafe behaviour. The framework was applied to Wuhan Lianghu Tunnel Project. The results demonstrate that the proposed framework can identify and predict workers’ unsafe behaviour, and therefore enable appropriate training to take place. The framework developed not only informs the health and safety personnel of unsafe behaviours that have occurred on-site but can also be used as an educational tool to inform people about the need to perform their work safely. It can be suggested that our research provides a new direction for exploring BBS management supported by computer vision technologies.

关 键 词：施工安全 行为安全 机器视觉 人工智能 行为矫正 

分 类 号：X92[环境科学与工程—安全科学]