Edge-Based PPE Detection for Urban Construction Under Realistic Visual Conditions

Automatic detection of Personal Protective Equipment (PPE) can improve road construction safety through continuous visual monitoring in urban environments. However, roadside deployment poses two challenges: limited computational resources on embedded devices and degraded visual conditions due to lighting variability and blurring. To date, the extent to which detection models maintain their performance under such visual degradations has not been systematically quantified. This work presents a systematic evaluation of YOLOv8-based PPE detection on edge devices. Multiple model variants with progressively increasing complexity are deployed on resource-constrained hardware and compared across heterogeneous inference formats in terms of detection accuracy, memory footprint, and end-to-end latency. A controlled sensitivity analysis further quantifies performance degradation under selected brightness variations and Gaussian blur designed to emulate real-world roadside conditions. The results indicate that lightweight configurations of the model provide the most favourable accuracy-latency tradeoff. The Nano variant achieves 88.9% mAP50 at 10.2 FPS on Edge TPU hardware. Severe blur reduces accuracy by more than 27 percentage points, whereas brightness shifts induce moderate degradation. Increasing model complexity does not yield proportional robustness improvements.