YOLOEB: a lightweight method for identifying violations of electric bicycles

Zhengyan Liu and Chaoyue Dai

International Journal of Low-Carbon Technologies, 2025, vol. 20, 1602-1615

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.

Keywords: object detection; image classification; convolutional neural network; electric bicycle detection; violation behavior recognition (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:1602-1615.

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