Abstract
This systematic review investigates the application of computer-vision technologies for automated monitoring of personal protective equipment compliance in industrial environments. This review followed the PRISMA 2020 guidelines and covered studies published between 2010 and 24 February 2026. It provides a structured synthesis of advances in deep learning-based object detection models, with particular emphasis on different YOLO variants, two-stage detectors such as Faster R-CNN, and emerging transformer-based and vision–language models. Model effectiveness, reported performance metrics, and dataset characteristics are comparatively examined, including their performance under practical operating conditions. Special attention is given to performance variability in real-world scenarios affected by illumination changes, occlusion, viewing angle variation, worker movement, computational constraints, and large-scale deployment requirements. The review also appraises the reporting quality and risk of bias of the included studies and identifies current research trends, methodological limitations, and the gap between laboratory validation and industrial implementation. It also outlines future directions for improving the reliability, cost-effectiveness, and practical application of computer vision-based personal protective equipment compliance systems.