Scale-Dependent Performance Analysis of YOLO26 and YOLOv11 for PPE Detection

Personal protective equipment (PPE) detection requires architectures balancing accuracy and computational efficiency for real-time safety monitoring. This study presents the first comprehensive benchmarking and systematic comparative evaluation of YOLO26 (released January 2026) against YOLOv11 across diverse PPE detection scenarios, with the primary goal of providing evidence-based deployment guidelines rather than proposing a new architecture. A total of 30 model configurations were evaluated across 5 model scales, 2 architectures, and 3 datasets under rigorously controlled conditions using identical hardware (NVIDIA A100-80GB), hyperparameters, and COCO-pretrained initialization across CHV (133 images, 6 classes), SHEL5K (1000 images, 3 classes), and SH17 (1620 images, 17 classes) datasets. Results reveal consistent scale-dependent patterns: YOLOv11 excels at nano and small scales across all datasets, while YOLO26 achieves superiority at large and X-Large scales with advantages ranging from 1.3 to 3.1 percent mAP50–95. An exploratory negative correlation (r=−0.98, n=3) between dataset size and YOLO26 performance advantage was observed; given the small number of data points, this should be interpreted as a preliminary finding warranting further investigation rather than a statistically robust relationship. YOLOv11 provides 15 to 20 percent faster training and 9 to 18 percent faster inference, while YOLO26 demonstrates superior parameter efficiency (0.0237 vs. 0.0233 mAP per million parameters). Findings provide evidence-based, conditional deployment guidance for industrial safety applications: YOLOv11 is recommended for latency-constrained edge scenarios at nano/small scales, while YOLO26 is preferred for accuracy-critical applications at large/X-Large scales with limited training data. These recommendations address key challenges in few-shot learning, small object detection, and data-scarce deployment regimes, and are intended as practical guidelines rather than claims of general architectural superiority.