AI-Driven Detection of Personal Protective Equipment Usage in Occupational Settings: Enhancing Workplace Safety
Intelligenza Artificiale, cilt.20, sa.2, ss.208-219, 2026 (ESCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 20 Sayı: 2
- Basım Tarihi: 2026
- Doi Numarası: 10.1177/17248035261463872
- Dergi Adı: Intelligenza Artificiale
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus
- Sayfa Sayıları: ss.208-219
- Anahtar Kelimeler: deep learning, occupational health and safety, PPE detection, YOLO
- Dokuz Eylül Üniversitesi Adresli: Evet
Özet
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.