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This work proposes a computer-vision intrusion detection approach for static hazardous areas using YOLO V5 for object detection, auxiliary modules for intrusion direction and posture, and an intrusion rule base with state-dependent sensitivities. Experiments show precision 96.05% and recall 90.05%, improving safety supervision effectiveness.",{"@graph":14,"@context":71},[15,34,54],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/human-intrusion-detection-in-static-hazardous-areas-at-construction-sites-deep-learningbased-method/648561/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":48,"encodingFormat":47,"isAccessibleForFree":49,"interactionStatistic":50},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/human-intrusion-detection-in-static-hazardous-areas-at-construction-sites-deep-learningbased-method/648561.png","ImageObject",300,407,{"name":42,"@type":43},"วิน","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-23",true,{"@type":51,"interactionType":52,"userInteractionCount":4},"InteractionCounter",{"@type":53},"ViewAction",{"@type":55,"mainEntity":56},"FAQPage",[57,63,67],{"name":58,"@type":59,"acceptedAnswer":60},"Why is human intrusion into static hazardous areas important for construction safety?","Question",{"text":61,"@type":62},"Human intrusion into static hazardous areas is identified as a significant cause of construction accidents, contributing to high incident rates on sites.","Answer",{"name":64,"@type":59,"acceptedAnswer":65},"What is the proposed deep learning approach for intrusion detection?",{"text":66,"@type":62},"The method uses YOLO V5 for object detection to extract image features, then applies auxiliary detection modules to recognize intrusion direction and posture, followed by rule-based judgment using a constructed intrusion rule base.",{"name":68,"@type":59,"acceptedAnswer":69},"What performance results does the case study report?",{"text":70,"@type":62},"The case study reports a precision of 96.05% and a recall of 90.05%, indicating effective intrusion detection performance.","https://schema.org",{"og:url":32,"og:type":73,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":75,"canonical":32},"index,follow",{"doc_id":77,"site_id":7},648561,1792794047,{"code":4,"msg":80,"data":81},"success",[82,86,90,94,99,104,109,113,118,121,125],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":83,"show_sort_weight":84,"slug":85},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":87,"show_sort_weight":88,"slug":89},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":91,"show_sort_weight":92,"slug":93},"Exam",70,"exam",{"id":95,"doc_module":4,"doc_module_name":25,"category_name":96,"show_sort_weight":97,"slug":98},5,"Comic",60,"comic",{"id":100,"doc_module":4,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},6,"Technology",50,"technology",{"id":105,"doc_module":4,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},7,"Healthcare",40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":111,"slug":112},8,30,"research-report",{"id":114,"doc_module":4,"doc_module_name":25,"category_name":115,"show_sort_weight":116,"slug":117},9,"Religion & Spirituality",20,"religion-spirituality",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":119,"show_sort_weight":116,"slug":120},"World Cup","world-cup",{"id":122,"doc_module":4,"doc_module_name":25,"category_name":123,"show_sort_weight":122,"slug":124},10,"Lifestyle","lifestyle",{"id":126,"doc_module":4,"doc_module_name":25,"category_name":127,"show_sort_weight":95,"slug":128},19,"General","general",{"code":4,"msg":80,"data":130},{"doc_id":77,"user_id":131,"nickname":42,"user_avatar":132,"doc_module":4,"category_id":110,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":4,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":138,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":78,"read_time":143},2336475104736,"https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222","Copyright ASCE; all rights reserved, including rights for text and data mining and training of artificial intelligence or similar technologies .  \nHuman Intrusion Detection in Static Hazardous Areas at Construction Sites: Deep Learning–Based Method  \nXinyu Mei 1 ; Xiaojing Zhou2 ; Feng Xu 3 ; and Zhipeng Zhang4  \nAbstract: Construction sites have complex environments and high accident rates. Human intrusion into static hazardous areas is a significant cause of accidents. Traditional engineering safety management mainly relies on manual methods, such as patrol inspection by safety supervisors, which is time-consuming and labor-intensive, and it is difficult to achieve a complete safety supervision. In recent years, the emergence of artificial intelligence technology and computer vision has provided a new scheme for intrusion detection. However, existing studies have used a single method for human intrusion judgment in static dangerous areas, without in-depth consideration of the influence of human posture, intrusion direction, and other factors. In this study, a computer vision–based intrusion detection method was developed, mainly aimed at static hazardous areas. The object detection was based on the You Only Look Once (YOLO) V5 module to extract the image feature information. Subsequently, the basic rule of intrusion judgment based on the key points of bounding boxes was formulated, in which the workers’ intrusion direction was recognized and postured using two auxiliary detection modules. Finally, the intrusion rule base was constructed as the basis for human intrusion detection, containing rules with different sensitivities for different intrusion states. The case study indicated that the precision and recall rate of the algorithm were 96.05% and 90.05%, respectively. Overall, this method can effectively address the defects of manual supervision in engineering safety management, reducing the probability of accident occurrence and enhancing safety at construction sites. DOI: 10.1061/(ASCE)CO.1943-7862.0002409. © 2022 American Society of Civil Engineers.  \nAuthor keywords: Hazardous areas; Intrusion detection; Intrusion posture; Machine vision; You Only Look Once (YOLO) V5; Deep learning.  \nIntroduction  \nThe construction industry has always had one of the highest incidences of occupational safety accidents, owing to the long construction cycle, extensive open-air work, and complex technical operations. According to an Occupational Safety and Health Administration (OSHA) summary, there were 1,061 worker fatalities in construction in 2019 in the United States, accounting for one in five worker deaths for the year (OSHA 2022a) . Falls are the leading cause of death (OSHA 2022b) . Similarly, according to the statistics of the Ministry of Housing and Urban-Rural Development of China (MOHURD 2020), from 2012 to 2019, 4,100 safety accidents occurred in housing and municipal works in China, resulting in 5,011 deaths. Among them, the proportion of falling accidents was much  \n1Undergraduate Research Assistant, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai Jiao Tong Univ., 800 Dongchuan  \nRd., Shanghai 200240, PR China. Email: [1749496562@qq.com](1749496562@qq.com)  \n2Undergraduate Research Assistant, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai Jiao Tong Univ., 800 Dongchuan  \nRd., Shanghai 200240, PR China. Email: [stuttering511@sjtu.edu.cn](stuttering511@sjtu.edu.cn)  \n3Associate Professor, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai Jiao Tong Univ., 800 Dongchuan Rd., Shanghai 200240, PR China (corresponding author). ORCID: [https://orcid](https://orcid)  \n.[org/0000-0001-9704-5528. Email: F.Xu@sjtu.edu.cn](org/0000-0001-970","cbCaitWkqF74niuy","https://ap.wps.com/l/cbCaitWkqF74niuy","pdf",2260837,15,"English","# Introduction\n## Problem background and safety context\n## Limitations of traditional manual supervision\n## Motivation for computer-vision intrusion detection","[{\"question\":\"Why is human intrusion into static hazardous areas important for construction safety?\",\"answer\":\"Human intrusion into static hazardous areas is identified as a significant cause of construction accidents, contributing to high incident rates on sites.\"},{\"question\":\"What is the proposed deep learning approach for intrusion detection?\",\"answer\":\"The method uses YOLO V5 for object detection to extract image features, then applies auxiliary detection modules to recognize intrusion direction and posture, followed by rule-based judgment using a constructed intrusion rule base.\"},{\"question\":\"What performance results does the case study report?\",\"answer\":\"The case study reports a precision of 96.05% and a recall of 90.05%, indicating effective intrusion detection performance.\"}]","Human Intrusion Detection in Static Hazardous Areas at Construction Sites - Deep Learning–Based Method | PDF",38]