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The study develops an automatic non-hardhat-use (NHU) detection approach, addressing limitations of near-field-only methods that struggle with far-field surveillance. A Faster R-CNN model is evaluated using over 100,000 worker frames sampled from 25 construction sites across more than one year. Image frames are categorized by visual conditions and fed into the model accordingly. Results show high precision, high recall, and fast processing, supporting improved safety inspection and supervision under diverse site conditions.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/detecting-non-hardhat-use-by-a-deep-learning-method-from-far-field-surveillance-videos/639766/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/detecting-non-hardhat-use-by-a-deep-learning-method-from-far-field-surveillance-videos/639766.png","ImageObject",300,407,{"name":92,"@type":93},"วิน","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-10-22",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is detecting non-hardhat-use important in construction safety?","Question",{"text":112,"@type":113},"Hardhats reduce the risk of severe head and neck injuries. Despite protective standards, incidents still happen when workers do not wear head protection, so automatic detection can strengthen supervision and prevention.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What method does the paper propose for non-hardhat-use detection?",{"text":117,"@type":113},"The paper proposes using a high-precision, high-speed Faster R-CNN approach to detect construction workers’ NHU in far-field surveillance videos.",{"name":119,"@type":110,"acceptedAnswer":120},"How was the Faster R-CNN performance evaluated?",{"text":121,"@type":113},"Over 100,000 construction worker image frames were randomly selected from far-field videos of 25 different construction sites over more than one year. Frames were classified by visual conditions and then input into Faster R-CNN.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},639766,1792705268,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143},2336475104736,"https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222","Automation in Construction 85 (2018) 1–9  \nContents lists available at ScienceDirect  \nAutomation in Construction journal [homepage:](homepage: www.elsevier.com/locate/autcon)[ www.elsevier.com/locate/autcon](homepage: www.elsevier.com/locate/autcon)  \n| Detecting non-hardhat-use by a deep learning method from far-field surveillance videos |  |  | |\n| --- | --- | --- | --- |\n| Qi Fanga,b, Heng Lib,⁎, Xiaochun Luob, Lieyun Dinga, Hanbin Luoa, Timothy M. Rosec, Wangpeng And\u003Cbr>a School of Civil Engineering & Mechanics, Huazhong University of Science & Technology, Wuhan, China b Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong c School of Civil Engineering and Built Environment, Queensland University of Technology, Australia d Department of Computing, The Hong Kong Polytechnic University, Hong Kong |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Construction safety\u003Cbr>Non-hardhat-use\u003Cbr>Far-field surveillance video Deep learning\u003Cbr>Faster R-CNN |  | Hardhats are an important safety measure used to protect construction workers from accidents. However, accidents caused in ignorance of wearing hardhats still occur. In order to strengthen the supervision of construction workers to avoid accidents, automatic non-hardhat-use (NHU) detection technology can play an important role. Existing automatic methods of detecting hardhat avoidance are commonly limited to the detection of objects in near-field surveillance videos. This paper proposes the use of a high precision, high speed and widely applicable Faster R-CNN method to detect construction workers' NHU. To evaluate the performance of Faster R-CNN, more than 100,000 construction worker image frames were randomly selected from the far-field surveillance videos of 25 different construction sites over a period of more than a year. The research analyzed various visual conditions of the construction sites and classified image frames according to their visual conditions. The image frames were input into Faster R-CNN according to different visual categories. The experimental results demonstrate that the high precision, high recall and fast speed of the method can effectively detect construction workers' NHU in different construction site conditions, and can facilitate improved safety inspection and supervision. |  |\n\n1. Introduction  \nConstruction is a high-risk activity requiring construction workers to operate in awkward postures, excessive lifting and high-intensity operations [1], which are key factors leading to occupational injuries. According to the United States' Bureau of Labor Statistics, the number of fatalities in the US has gradually increased from 849 to 985 between 2012 and 2015 [2]. Similarly, according to the UK Health and Safety Executive (HSE), 38 construction workers suffered fatal injuries in Great Britain between April 2014 and March 2015, while this figure rose to 45 [3] during the same period the following year. Such statistics reinforce the need for greater safety measures to reduce the occurrence of construction accidents.  \nThe consequences of head injuries are the most serious of all construction accidents. Although accidents involving legs, feet and toes most lead to some injury, those involving head and neck are often fatal [4]. Many head and neck injuries are caused by falling from height or being stuck by vehicles and other moving plant and equipment. From 2003 to 2010, 2210 construction workers in the United States died as a  \nresult of traumatic brain injuries [5,6], accounting for 24% of the total number of deaths from construction accidents.  \nWearing a hardhat is an effective protective measure for minimizing the risk of traumatic brain injury. In construction accidents, hardhats protect workers by resisting penetration by objects, absorbing shock from direct blows to the head by objects and reducing electrical shock hazards. The United States Occupational Safety & Health Administration (OSHA) has two ","cbCaidZGqAyzKqzI","https://ap.wps.com/l/cbCaidZGqAyzKqzI","pdf",1316485,"English","# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"Why is detecting non-hardhat-use important in construction safety?\",\"answer\":\"Hardhats reduce the risk of severe head and neck injuries. Despite protective standards, incidents still happen when workers do not wear head protection, so automatic detection can strengthen supervision and prevention.\"},{\"question\":\"What method does the paper propose for non-hardhat-use detection?\",\"answer\":\"The paper proposes using a high-precision, high-speed Faster R-CNN approach to detect construction workers’ NHU in far-field surveillance videos.\"},{\"question\":\"How was the Faster R-CNN performance evaluated?\",\"answer\":\"Over 100,000 construction worker image frames were randomly selected from far-field videos of 25 different construction sites over more than one year. Frames were classified by visual conditions and then input into Faster R-CNN.\"}]","Detecting non-hardhat-use by a deep learning method from far-field surveillance videos | PDF",23]