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Accidents frequently result from inadequate safety distances and poorly managed worker–machine interactions, highlighting the need for advanced safety management solutions. This study develops and validates an innovative hazard warning system that leverages deep learning-based image recognition (YOLOv7) and Internet of Things (IoT) modules to enhance construction site safety. 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Leveraging Deep Learning and Internet of Things for Dynamic Construction Site Risk Management. Buildings 2025, 15, 1325. [https://](https://)[ ](https://)[doi.org/10.3390/buildings15081325](doi.org/10.3390/buildings15081325)  \n[Copyright:](Copyright:) © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Department of Civil Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan; [yrwang@nkust.edu.tw](yrwang@nkust.edu.tw)  \n2 ASC Digital Technology Co., Ltd., Kaohsiung 814017, Taiwan; [sointheuniverse0160@gmail.com](sointheuniverse0160@gmail.com)  \n* Correspondence: [i109141101@nkust.edu.tw](i109141101@nkust.edu.tw)  \nAbstract: The construction industry faces persistent occupational health and safety challenges, with numerous risks arising from construction sites’ complex and dynamic nature. Accidents frequently result from inadequate safety distances and poorly managed work-er– machine interactions, highlighting the need for advanced safety management solutions. This study develops and validates an innovative hazard warning system that leverages deep learning-based image recognition (YOLOv7) and Internet of Things (IoT) modules to enhance construction site safety. The system achieves a mean average precision (mAP) of 0.922 and an F1 score of 0.88 at a 0.595 confidence threshold, detecting hazards in under 1 s. Integrating IoT-enabled smart wearable devices provides real-time monitoring, delivering instant hazard alerts and personalized safety warnings, even in areas with limited network connectivity. The system employs the DIKW knowledge management framework to extract, transform, and load (ETL) high-quality labeled data and optimize worker and machinery recognition. Robust feature extraction is performed using convolutional neural networks (CNNs) and a fully connected approach for neural network training. Key innovations, such as perspective projection coordinate transformation (PPCT) and the security assessment block module (SABM), further enhance hazard detection and warning generation accuracy and reliability. Validated through extensive on-site experiments, the system demonstrates significant advancements in real-time hazard detection, improving site safety, reducing accident rates, and increasing productivity. The integration of IoT enhances scalability and adaptability, laying the groundwork for future advancements in construction automation and safety management.  \nKeywords: construction safety; hazard warning system; deep learning; IoT integration; safety management  \n1. Introduction  \nThe construction industry faces significant safety and health challenges, with higher accident and fatality rates than other sectors [1,2] . In Taiwan, for example, the construction sector experiences a disproportionately high number of workplace incidents [3], with a fatality rate three times the national average, underscoring the urgent need for improved safety measures [4] . This reflects a global trend where construction safety remains a critical concern despite widespread safety standards and regulations.  \nConstruction sites are dynamic environments where workers, machinery, and materials continuously interact, creating complex safety challenges [5] . In Taiwan, these challenges are intensified by diverse, concu","cbCaioImKuqsNB7v","https://ap.wps.com/l/cbCaioImKuqsNB7v","pdf",8528529,24,"English","# Abstract\n# Introduction\n# Literature Review\n## Construction Industry Safety","[{\"question\":\"What problem does the study address in construction sites?\",\"answer\":\"The study targets occupational health and safety risks caused by the complex, dynamic nature of construction sites, especially accidents related to insufficient safety distances and poor management of worker–machine interactions.\"},{\"question\":\"How does the proposed hazard warning system detect risks?\",\"answer\":\"It uses deep learning-based image recognition with YOLOv7 for real-time multi-object detection of workers and machinery, then measures relevant distances to generate precise hazard detection and warnings.\"},{\"question\":\"What role does IoT play in the safety solution?\",\"answer\":\"IoT-enabled smart wearable devices provide real-time monitoring and instant hazard alerts, enabling personalized safety warnings and supporting operation even when network connectivity is limited.\"}]","Leveraging Deep Learning and Internet of Things for Dynamic Construction Site Risk Management | PDF"]