Artificial Intelligence System Based Personal Protective Equipment Detection for Construction Site Safety using YOLOv8

Abstract: This paper addresses the limitations of current deep learning models for detecting Personal Protective Equipment (PPE) on construction sites, where performance enhancement is crucial. This paper use You Only Look Once (YOLO) architecture, focusing on ten categories: 'Hardhat', 'Mask', 'NO-Hardhat', 'NO-Mask', 'NO-Safety Vest', 'Person', 'Safety Cone', 'Safety Vest', 'machinery', 'vehicle'. A new high-quality dataset, named PPE dataset from Roboflow, was created, comprising 1,330 images that reflect real construction environments, various poses, angles, distances, and multiple PPE types. Among the evaluated models, YOLO v8 achieved the highest mean Average Precision (mAP) of 87.55%, while YOLO v8 demonstrated the fastest processing speed at 52 images per second on a GPU. The study involved training a model using 2,934 images and validating it with 816, resulting in a 95% mean Average Precision (mAP). It underscores the significant role of artificial intelligence in enhancing safety management and occupational health within the construction sector. This research serves as a foundation for future advancements in AI-driven safety measures, addressing the urgent need for innovative strategies to minimize workplace risks and elevate compliance standards in the industry.