Digital monitoring of the use of Personal Protective Equipment at industrial facilities using neural network architectures

Abstract

Background: Ensuring employees occupational safety at the oil and gas industry facilities remains a relevant objective, as far as traditional monitoring methods for the use of Personal Protective Equipment (PPE) are based on manual visual inspections and are susceptible to human factor. Most existing computer vision systems are limited to detecting only a small number of 2–6 classes of PPE categories and to verifying the anatomical consistency between detected protective equipment and the corresponding body parts of employees. Aim: Developing and validating a digital monitoring method for compliance with PPE use requirements based on neural network architectures, integrating algorithms for object detection, human pose estimation, and anatomical matching of PPE elements in real time mode. Materials and methods: A unique dataset of 16352 images (after augmentation) containing 13 object classes, including 6 types of PPE and 6 negative classes was created for training the model. The YOLOv8 model was used for object detection, and HRNet for human pose estimation. A two-tier video stream processing architecture was implemented, combining object tracking (BoT-SORT), spatial and anatomical matching and TensorRT quantization to improve system performance. Results: During the training phase, the YOLOv8 model achieved Precision = 0.98, Recall = 0.97, and F1 = 0.94. When testing the developed system on 16 video files obtained from industrial sites, the system achieved a precision of 96.58%, a recall of 68.48%, and an F1 score of 0.8014. Detecting small objects (gloves) in cropped images improves detection efficiency by 2-3 times compared to full-frame processing. Conclusion: The developed approach, combining the YOLOv8 and HRNet models and an anatomical matching algorithm, provides effective digital monitoring of compliance with PPE requirements in real-world production conditions. The obtained results confirm the potential of its application in the development of intelligent industrial safety monitoring systems at industrial facilities.