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

Ensuring proper use of personal protective equipment (PPE), especially helmets, is essential for workplace safety. Conventional object detectors often fail to distinguish whether a helmet is worn correctly, and existing approaches relying on single-model pipelines are prone to localization errors and false alarms. Moreover, most prior studies do not guarantee real-time performance. To resolve these challenges, we propose a lightweight multimodal approach that integrates a YOLO11-based object detector with a pose estimation model, achieving higher F1 scores and lower false alarm rates while maintaining real-time performance.

Authors

• Jeong Hwan Ryu Department of Computer Science, Chungbuk National University, Cheongju, 28644, South Korea
• Azimjon Akhtamov Department of Computer Science, Chungbuk National University, Cheongju, 28644, South Korea
• Md Azher Uddin School of Mathematical and Computer Science, Heriot-Watt University, Dubai 501745, United Arab Emirates
• Aziz Nasridinov Chungbuk National University

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