Automated PPE compliance monitoring in industrial environments using deep learning-based detection and pose estimation
Leopoldo López, Jonay Suárez–Ramírez, Miguel Alemán-Flores, Nelson Monzón
Abstract
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.