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This study addresses the challenge of detecting helmet violations among motorcyclists by proposing a novel approach based on the BF-YOLOv7 model, which enhances the accuracy of video analytics. Additionally, we incorporate the PRB-FPN6-MSP model to further refine detection results. Our experiments, conducted on the 2024 AI City Challenge Track 5 benchmark dataset, demonstrate the effectiveness of our approach. The results indicate that our method performed exceptionally well across 100 test videos, achieving rankings of 11th and 16th on the public leaderboard, outperforming 43 competing teams.

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BF-YOLOv7: Enhancing Helmet Rule Violation Detection

  • 会议论文

  • 发表日期：

    2026年

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  • Chun-Ming Tsai,
  • Jun-Wei Hsieh,
  • Ming-Ching Chang

摘要

This study addresses the challenge of detecting helmet violations among motorcyclists by proposing a novel approach based on the BF-YOLOv7 model, which enhances the accuracy of video analytics. Additionally, we incorporate the PRB-FPN6-MSP model to further refine detection results. Our experiments, conducted on the 2024 AI City Challenge Track 5 benchmark dataset, demonstrate the effectiveness of our approach. The results indicate that our method performed exceptionally well across 100 test videos, achieving rankings of 11th and 16th on the public leaderboard, outperforming 43 competing teams.