AI-Driven Detection of Personal Protective Equipment Usage in Occupational Settings: Enhancing Workplace Safety
Intelligenza Artificiale, vol.20, no.2, pp.208-219, 2026 (ESCI, Scopus)
- Publication Type: Article / Article
- Volume: 20 Issue: 2
- Publication Date: 2026
- Doi Number: 10.1177/17248035261463872
- Journal Name: Intelligenza Artificiale
- Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus
- Page Numbers: pp.208-219
- Keywords: deep learning, occupational health and safety, PPE detection, YOLO
- Dokuz Eylül University Affiliated: Yes
Abstract
The primary objective of this study is to assess the effect of systematic dataset augmentation on the accuracy of real-time, vision-based Personal Protective Equipment (PPE) detection systems in occupational environments. The PPEDS-1000 dataset was employed, comprising 1,000 expertly annotated images across four PPE usage categories: worker (W), worker with helmet (WH), worker with vest (WV), and worker with both helmet and vest (WHV). An augmented dataset (PPEDS-2600) was derived via controlled geometric transformations (horizontal and vertical flips), additive Gaussian noise, and Gaussian blur. Each dataset is partitioned using an 80/10/10 train–validation–test split and utilized to train five YOLOv8 model variants (nano through extra-large). The evaluation metrics include precision, recall, F1-score, mean average precision at an IoU threshold of 0.5 (mAP50), and mean average precision averaged over IoU thresholds from 0.5 to 0.95 (mAP50-95). The experimental results demonstrate that augmentation elevates mAP50 from 77.7% on PPEDS-1000 to 94.8% on PPEDS-2600, thereby substantiating the hypothesis that targeted augmentation markedly enhances detection performance. The findings indicate that the present work establishes a rigorous benchmark for real-time PPE compliance monitoring.