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YOLOEB: a lightweight method for identifying violations of electric bicycles

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  • Zhengyan Liu
  • Chaoyue Dai

Abstract

With the rise in traffic accidents due to the popularity of electric bicycles, automatic violation detection has become difficult. Machine vision-based detection faces challenges such as labor-intensive data annotation and decreased accuracy. This study presents the YOLOEB algorithm, which combines YOLOv7 and RepVGG block reparameterization to improve detection accuracy while maintaining inference time. YOLOEB uses Resnet-50 for classification and regression positioning for detection boxes. When evaluated on the Dataset-Det, YOLOEB achieved 98.5% detection accuracy and 97.2% recall rate, reducing annotation efforts and increasing processing speed to meet practical application requirements.

Suggested Citation

  • Zhengyan Liu & Chaoyue Dai, 2025. "YOLOEB: a lightweight method for identifying violations of electric bicycles," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 1602-1615.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:1602-1615.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf018
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