Automated PPE compliance monitoring in industrial environments using deep learning-based detection and pose estimation

This paper presents an AI framework for automated detection of personal protective equipment (PPE) compliance in complex construction and industrial environments. Ensuring health and safety standards is essential for protecting workers engaged in construction, repair, or inspection activities. The framework leverages deep learning techniques for worker detection and pose estimation to enable accurate PPE identification under challenging conditions. The framework components are replaceable, and employ the InternImage-L detector for worker detection, ViTPose for pose estimation, and YOLOv7 for PPE recognition. A duplicate removal stage, combined with pose information, ensures PPE items are accurately assigned to individual workers. The approach addresses challenges like shadows, partial occlusions, or densely grouped workers. Evaluated on diverse datasets from real-world industrial settings, the framework achieves competitive precision and recall, particularly for critical PPE like helmets and vests, demonstrating robustness for safety monitoring and proactive risk management. • AI framework integrates worker detection, pose estimation, and PPE recognition. • Collision detection and duplicate removal improve PPE assignment in occlusions. • An ablation study evaluates the relevance and optimal strategy of each module. • Extensive evaluations demonstrate adaptability across diverse real-world conditions. • The framework outperforms state-of-the-art models in recall and F1 score.