Abstract
This article investigates the effects of dataset size and class imbalance on the performance of You Only Look Once (YOLO)v10-based personal protective equipment (PPE) detection systems. Six YOLOv10 configurations (n, s, m, b, l, and x) were evaluated using domain-specific datasets from construction, industrial, and medical contexts. A novel mathematical model is proposed to characterize the nonlinear relationship between dataset size and detection performance (mean Average Precision (mAP)@50), revealing a saturation threshold beyond which additional data yield diminishing performance gains (R² = 0.981). The analysis further demonstrates that the superior accuracy of more complex YOLOv10-x models deteriorates significantly under conditions of pronounced class imbalance. These findings underscore the critical importance of balanced and sufficiently large datasets for optimizing detection accuracy in real-world safety applications.