Enhancing Firefighter PPE Compliance Through Deep Learning and Computer Vision

Ensuring firefighter safety in high-risk environments requires strict adherence to Personal Protective Equipment (PPE) protocols. This study presents an automated real-time detection system for PPE using deep learning and computer vision techniques, aiming to improve PPE compliance and overall safety monitoring. The research employs advanced object detection models, specifically YOLOv10 and YOLOv11 (You Only Look Once), to identify critical PPE components such as helmets, gloves, boots, and self-contained breathing apparatus (SCBA) units. A custom-annotated dataset of firefighter images was developed to train and evaluate both models using standard performance metrics such as precision, recall, mAP, F1-score, and Intersection over Union (IoU). The results show that YOLOv11 outperformed YOLOv10, achieving a higher [email protected] score of 0.646 compared to 0.586, with improved detection of small and partially occluded objects and a reduction in training time by 11%. YOLOv11 showed improved detection accuracy for small and partially blocked objects and reduced training time by 11%, while maintaining real-time efficiency. The system generates instant alerts when PPE is missing, minimizing reliance on manual monitoring and improving situational awareness in real-time. This research reinforces the role of AI in public safety and AI-powered automation in enhancing critical public safety operations. By integrating deep learning and computer vision into PPE monitoring systems, the study contributes to developing intelligent, responsive solutions aligned with modern safety standards.