基于计算机视觉的塔吊危险区域入侵预警  

Crane danger zone intrusion warning based on computer vision

作　　者：吴立舟 李华[1] 李典斌 吴昱锦 刘攀旺 薛曦澄 WU Lizhou;LI Hua;LI Dianbin;WU Yujin;LIU Panwang;XUE Xicheng(School of Resources Engineering,Xi'an University of Architecture and Technology,Xi'an Shaanxi 710055,China;Guangzhou Zhonghaida Satellite Navigation Technology Co.,Ltd.,Guangzhou Guangdong 511400,China;China Construction Third Bureau Group Beijing Co.,Ltd.,Langfang Hebei 065000,China;Northwest Branch,China Construction Eighth Engineering Bureau Co.,Ltd.,Xi'an Shaanxi 710075,China)

机构地区：[1]西安建筑科技大学资源工程学院,陕西西安710055 [2]广州中海达卫星导航技术股份有限公司,广东广州511400 [3]中建三局集团北京有限公司,河北廊坊065000 [4]中国建筑第八工程局有限公司西北分公司,陕西西安710075

出　　处：《中国安全科学学报》2024年第7期139-145,共7页China Safety Science Journal

基　　金：陕西省建设厅科技发展计划项目(2020-K32);西安建科大工程技术项目(XAJD-YF23N010)。

摘　　要：为解决建筑施工中塔吊作业存在的危险区域识别场景复杂的问题,利用计算机视觉技术,提出一种塔吊危险区域入侵预警方法。该方法结合塔吊危险区域动态判定和计算机视觉检测施工现场人员安全帽、安全带佩戴情况以及塔吊下方人员意外入侵,同时,在YOLOv5算法基础上修改注意力模型,并开发窗口交互式检测软件。结果表明:人的入侵行为和安全防护装备在模型中的识别准确率均达85%以上,具有较高准确度。该方法可在塔吊施工场景下进行有效应用,将塔吊固定式危险区域划分为动态塔吊危险区域,并实时监测人员意外入侵及预警。To address the complex scenarios of identifying danger zones in tower crane operations during construction,an early warning method of tower crane danger zone was proposed using computer vision technology.This method combined dynamic determination of tower crane danger zones with computer vision to detect personnel wearing situations of safety helmets and safety belt at the construction site and the inadvertent intrusion beneath the tower crane.Additionally,the YOLOv5 algorithm was adapted with attention models,and interactive window detection software was developed.Results indicate that the recognition accuracy of this model for human intrusion behavior and personal protective equipment exceeds 85%,demonstrating high precision.This method can be effectively applied in tower crane construction scenarios,optimizing fixed danger zone delineation to dynamic tower crane danger zones,and providing real-time monitoring of inadvertent personnel intrusion with warnings.

关 键 词：计算机视觉 塔吊危险区域 入侵预警 YOLOv5 安全管理 

分 类 号：X924.3[环境科学与工程—安全科学]