Enhancing Construction Worker Safety through YOLOv8-Based PPE Detection and Sensor-Driven Smart Helmets

In order to improve worker safety in construction settings, this study proposes an integrated safety system that combines real-time sensor monitoring with deep learning. To ensure adherence to PPE regulations, the first component uses the YOLOv8 algorithm for precise and effective helmet detection. In order to monitor motion and environmental threats and send real-time alerts, the second component includes a Smart Helmet that is outfitted with a MEMS sensor, gas sensor, DHT11, and GSM module. When combined, these modules provide enhanced on-site safety and a proactive approach to accident avoidance.