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

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 Department of Computer Science, Chungbuk National University, Cheongju, 28644, South Korea

DOI:

https://doi.org/10.1609/aaai.v40i48.42274

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.
AAAI-26 / IAAI-26 / EAAI-26 Proceedings Cover

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Published

2026-03-14

How to Cite

Ryu, J. H., Akhtamov, A., Uddin, M. A., & Nasridinov, A. (2026). A Lightweight Safety Helmet Compliance Detection via Multimodal Fusion (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 40(48), 41373–41374. https://doi.org/10.1609/aaai.v40i48.42274

Issue

Vol. 40 No. 48: EAAI-26 AI for Education, Model AI Assignments, AAAI-26 Emerging Trends, Doctoral Consortium, Student Abstracts, Undergraduate Consortium and Demonstrations

Section

AAAI Student Abstract and Poster Program