Hyperparameter Optimization for Enhancing Personal Protective Equipment (PPE) Detection Performance Using YOLO-Based Deep Learning

Consistent use of personal protective equipment (PPE) reduces exposure to industrial hazards, yet continuous compliance monitoring remains difficult in fast-moving workplaces. This study evaluates lightweight, real-time PPE detectors using two purpose-built datasets: PPEDS-1000 (1,000 manually annotated real-world images) and PPEDS-2600 (an augmented extension designed to reflect deployment variability). YOLOv13-nano and YOLOv13-small, implemented from the publicly available iMoonLab repository, were trained as untuned baselines, while optimization focused on YOLOv8 due to its mature tooling and scalable lightweight variants. Optuna-based hyperparameter optimization was restricted to YOLOv8-nano and YOLOv8-small to preserve deployment feasibility, tuning key training factors such as learning rate, batch size, and weight decay with validation mAP50 as the objective. Compared with pre-optimization baselines, the single-run best configurations achieved absolute gains of up to 2.5 percentage points in mAP50 and 3.2 percentage points in mAP50–95. However, an exploratory repeated-run analysis suggests that the stability of these gains is dataset-dependent. On the smaller PPEDS-1000 dataset, tuning benefits appeared seed-sensitive, whereas on the larger and augmented PPEDS-2600 dataset, tuning showed more consistent behavior together with lower variance across random initializations. Overall, the findings indicate that structured hyperparameter tuning is promising for deployment-oriented PPE monitoring, but its ability to yield stable gains appears to depend on adequate dataset scale and representative variability.