Development and Optimization of a Construction Personal Protective Equipment (PPE) Detection Model on YOLOv8 Architecture - Zidan Rafindra Utomo; Prajanto Wahyu Adi; Priyo Sidik Sasongko; Gohar Rahman (Department of Informatics, Universitas Diponegoro, 2025)
Workplace safety in the construction sector remains a critical issue due to frequent accidents caused by non-compliance with Personal Protective Equipment (PPE) regulations. Manual supervision is inefficient and prone to errors, necessitating an automated detection approach. The prior YOLOv5 version trained on the Construction Safety dataset from Roboflow-100, achieves a mean Average Precision (mAP@0.50) of 0.867. However, class imbalance, particularly the underrepresentation of