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The YOLOEB algorithm improves detection accuracy and reduces annotation efforts for electric bicycles, suggesting robust solutions for violation identification.
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.
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Liu et al. (2025) studied this question.
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