Performance Analysis of YOLO, Faster R-CNN, and DETR for Automated Personal Protective Equipment Detection

Abstract

Automated monitoring of Personal Protective Equipment (PPE) is crucial for enhancing safety in high-risk environments like construction sites, yet selecting the optimal detection model requires careful evaluation of accuracy versus efficiency trade-offs. This study presents a comparative performance analysis across distinct object detection paradigms represented by YOLO (YOLOv8, YOLOv11n), Faster R-CNN, and DETR to benchmark their suitability for real-time PPE detection. However, this study moves beyond a simple technical benchmark by also proposing a logical process to transform raw model detections (e.g., 'person', 'hardhat') into actionable compliance verification information (e.g., 'Compliant'/'Non-Compliant'). Using a curated construction site safety dataset, models were evaluated based on standard accuracy metrics (including mAP@.5:.95) and efficiency measures (inference latency). Results indicate that DETR and YOLOv11n achieved the highest overall accuracy with an identical mAP@.5:.95 of 0.770, closely followed by YOLOv8 (0.763), while the YOLO family demonstrated significantly superior real-time efficiency (6-7 ms latency). Faster R-CNN recorded a lower mAP (0.703) and the highest latency. Conclusively, YOLOv11n offers the most compelling balance for the detection phase, and the proposed logical process provides a practical method for integrating this technical output into automated safety monitoring systems.