Abstract
Personal protective equipment (PPE) is critical for infection control in healthcare, protecting workers and patients from infection risks. The COVID-19 pandemic further highlighted the importance of correct PPE use, yet adherence to U.S. Centers for Disease Control and Prevention guidelines remains inconsistent. Continuous human monitoring of PPE adherence is impractical because it is labor-intensive and may expose observers to infection risk. Automated monitoring is a promising alternative, but reliable PPE assessment in clinical videos remains difficult due to occlusion and subtle differences between adherence levels. To address these challenges, we propose SAFE - Smart Adherence detection Framework for PPE, a cascaded computer vision system for real-time monitoring of PPE wearing status, including complete, incomplete, and absent cases, with a focus on gowns and masks. SAFE uses a two-stage design: Stage 1 detects gown status and localizes head regions, and Stage 2 classifies mask status from head crops. We evaluate SAFE on R2PPE, a ceiling-view trauma-room simulation dataset with dense PPE annotations and complex scenes. SAFE improves overall average precision from 0.48 to 0.67 and increases mask-class average precision by 0.33 compared to a baseline one-stage detector. We further validate SAFE across modern detector backbones, including transformer-based detectors, and on real-case trauma-room data using class-level and alarm-level criteria, improving class-level mask accuracy from 0.59 to 0.65 while maintaining a high alarm-level recall of 0.98. SAFE could enhance PPE monitoring with minimal human intervention, providing a scalable solution for improving infection control in healthcare settings.