Following safety procedures is essential to preventing accidents and injuries on construction sites, which are high-risk locations. Among these protocols, wearing safety helmets is a fundamental requirement for worker protection. Nevertheless, manual helmet usage monitoring isn't always successful, particularly on big construction sites. The YOLOv8 (You Only Look Once, version 8) object detection algorithm and machine learning are used in this research to create an intelligent, real-time helmet detection system.The goal is to automate the process of monitoring helmet compliance among workers at construction sites through image and video analysis. A dataset of photos featuring people wearing and not wearing helmets is used to train the system. Using YOLOv8's advanced detection capabilities, the model identifies and classifies people based on helmet usage in real time. When a violation is detected (i.e., a person not wearing a helmet), the system can trigger alerts or log the event for further action. The proposed solution not only enhances safety enforcement but also minimizes the need for constant human supervision. It is scalable, fast, and accurate—making it a practical tool for smart construction site management.
Giridipalli Laxmana Dora (Thu,) studied this question.