Nurbaity Sabri, Hafizul Ikram Azmi, Raihah Aminuddin, Shafaf Ibrahim, Budi Sunarko
Ensuring Personal Protective Equipment (PPE) compliance in the food industry is vital for maintaining hygiene standards, yet manual monitoring remains error-prone. This study introduces an automated PPE detection system using the YOLOv7 algorithm, specifically targeting aprons, hairnets, and footwear within the Malaysian food sector. A dataset of 1,500 images was expanded to 2,859 through mosaic augmentation to enhance model generalization. Among the variants tested, YOLOv7-d6 emerged as the most reliable and balanced configuration, achieving an mAP (0.5) of 0.918. While YOLOv8-l achieved a slightly higher mAP (0.5) of 0.923, it exhibited a critical imbalance between precision and recall, posing a risk of missed violations in safety-sensitive environments. In contrast, YOLOv7-d6 maintained a balanced precision (0.934) and recall (0.930), ensuring high detection reliability. Real-time validation further confirmed the model’s efficacy, with detection accuracies of 88.24% for aprons, 90.91% for footwear, and 83.33% for hairnets. These results demonstrate that the YOLOv7-d6 framework serves as a robust automated monitoring system for strengthening food safety compliance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2026.
Faculty of Computer and Mathematical Sciences (FSKM), Universiti Teknologi MARA (UiTM), Melaka, Malaysia; Faculty of Computer and Mathematical Sciences (FSKM), Universiti Teknologi MARA (UiTM), Seksyen 11, Selangor, Shah Alam, 40100, Malaysia; Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Semarang (UNNES), Sekaran, Gunungpati, Central Java, Semarang, 50229, Indonesia
