WORKERS PERSONAL PROTECTIVE EQUIPMENT DETECTION USING YOLO

Computer Engineering

To enhance safety in various settings and address the risks associated with personal protective equipment (PPE) non-compliance, effective PPE detection systems are essential. Closed-circuit television (CCTV) cameras are commonly employed for this purpose. This study conducts a thorough investigation into cutting-edge methods for leveraging CCTV footage to improve PPE compliance monitoring, with a specific emphasis on the YOLO (You Only Look Once) algorithm. The primary objective of this research is to assess the performance of the YOLO algorithm in PPE detection. By training the YOLO model on a substantial dataset, the study utilizes key evaluation metrics such as accuracy, precision, recall, and F1 score to assess its performance. The primary objective of this research is to assess the performance of the YOLO algorithm in PPE detection. By training the YOLO model on a substantial dataset, the study utilizes key evaluation metrics such as accuracy, precision, recall, and F1 score to assess its performance.

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