Safety Helmet Detection System Based on YOLOv5 Algorithm

As a major infrastructure powerhouse, ensuring the safety of construction workers in China is crucial. Construction site accidents are mainly caused by harsh working environments, improper operations by workers, and inadequate supervision by managers. Among these, wearing a safety helmet properly can significantly reduce the consequences of accidents. Currently, checking whether workers are wearing safety helmets on construction sites is primarily done manually, a method that is time-consuming and inefficient. With the development of object detection technologies, deep learning-based methods have gradually become the focus of research. Therefore, this paper takes the YOLOv5s algorithm as the technical foundation to design and implement a real-time safety helmet detection system.