A Lightweight Safety Helmet Compliance Detection via Multimodal Fusion (Student Abstract)

Ensuring proper use of personal protective equipment (PPE), especially helmets, is critical for workplace safety. Conventional object detectors often fail to distinguish whether a helmet is worn correctly, and existing approaches relying on ROI cropping or single-model pipelines are prone to localization errors and false alarms. Moreover, most prior studies do not guarantee real-time operation under lightweight deployment constraints. To address these challenges, we propose a lightweight YOLO11-based object detector combined with a pose estimation model, achieving both higher F1 score and lower false alarm rates while maintaining real-time performance.