Edge-Based Multi-Object PPE Compliance Monitoring: A Critical Evaluation of YOLOv8n Structural Reliability on Resource-Constrained Platforms

Occupational Safety and Health (OSH) requires continuous monitoring of Personal Protective Equipment (PPE) compliance, while manual supervision is limited by coverage, continuity, and human observation. This study evaluates an edge-based PPE monitoring prototype using YOLOv8n on a Raspberry Pi 4 for detecting six PPE classes: helmet, safety glasses, mask, safety vest, gloves, and safety shoes. The system uses a 2K QHD autofocus USB camera mounted on a static tripod and integrates object detection with a tiered SOP decision module and physical warning output. Model performance was evaluated using precision, recall, F1-score, Average Precision (AP), mean Average Precision (mAP), FPS, and inference time. Field evaluation included six initial test frames, distance tests at 3-5 m with frontal and side orientations, additional testing at 2 m, a dedicated safety-glasses test at approximately 1 m, and a qualitative indoor test. The model achieved an overall mAP@0.5 of 0.737. Helmet, safety vest, and safety shoes showed the strongest AP@0.5 values, whereas smaller or less visually distinct PPE classes remained more difficult to detect. Edge deployment achieved 8.4-11.6 FPS with inference times of 86.1-119.3 ms. Across the six initial test frames, five SOP decisions were correct, resulting in 83.3% decision accuracy and a 16.7% false alarm rate. The results demonstrate the feasibility of limited real-time PPE monitoring on Raspberry Pi 4 while showing that reliability remains sensitive to object size, distance, viewpoint, and occlusion.