Real-time personal protective equipment compliance detection using you only look once - Nurhanisah, Aminuddin; Nor Azuana, Ramli; Pratondo, Agus (IEEE, 2024)
Developing automated systems to detect and identify protective equipment in working sites is not just a technical accomplishment but also a significant step towards improving the workplace, especially in high-risk areas like construction and manufacturing sites. The objective of detecting safety equipment is to identify and classify the equipment worn by workers to ensure compliance with safety standards. The proposed research work employs the system of object detection from CCTV videos around the working sites of company XYZ through a framework that includes image acquisition, data collection, and video data pre-processing, with the implementation of various deep learning algorithms using state-of-the-art models such as You Only Look Once (YOLO) including YOLOv5, YOLOv7, and YOLOv8. The primary dataset, shared through a cloud server for confidentiality, is used for training. The model's performance is evaluated using metrics such as confusion matrix, accuracy, precision, recall, mean average precision (mAP), and losses. Results indicate that YOLOv8 exceeds the performance of the other YOLO models, achieving precision, recall, and mAP of over 90% for all identified classes: HELMET, NOHELMET, SAFETYJACKET, NOSAFETYJACKET. Further analysis comparing the selected models based on the confusion matrix shows that YOLOv8 demonstrates more accurate predictions, while YOLOv5 and YOLOv7 primarily detect background, leading to minimal object detection within the bounding boxes.