<![CDATA[Compliance with Personal Protective Equipment (PPE), such as safety shoes, is a crucial challenge in high-risk work environments, including for firefighters. Negligence in PPE usage is a leading cause of workplace accidents. This study aims to analyze the performance of the YOLOv8 object detection model in a real-time monitoring system designed to detect the use of safety shoes. The research method includes system design, image dataset collection, YOLOv8 model training, and performance evaluation using standard metrics. The performance analysis shows excellent model performance, achieving a precision of 97%, recall of 94.9%, and a mean Average Precision (mAP) of 97.5%. Furthermore, functional testing of the system resulted in a 90% user satisfaction rate. These results indicate that YOLOv8 is an effective and reliable method for automated monitoring and has great potential to minimize workplace accidents caused by negligence in PPE use.]]>
Copyrights © 2025
