Authors
Shaohui Zhong, Xiaofei Liu, Caihua Chen
Published in
Scientific reports. Jun 23, 2026. Epub Jun 23, 2026.
Abstract
In response to the increasingly serious traffic safety issues and regulatory challenges of electric bicycles (e-bikes), this study proposes an advanced multi-task detection model called P-YOLOv10. The model aims to achieve end-to-end unified recognition of riding safety factors and fine-grained regional attributes of license plates. To address inaccurate small-object detection and difficulty in distinguishing fine-grained features in complex real-world scenes, P-YOLOv10 introduces systematic optimizations based on the latest YOLOv10 architecture. First, it integrates the Selective Channel-Spatial Attention (SCSA) module to enhance the network's ability to capture key local features. Second, it adopts the minimum point distance intersection over union (MPDIoU) loss function to improve bounding box regression accuracy, especially for small objects such as license plates. Finally, it uses the Gaussian error linear unit (GELU) activation function to improve nonlinear representation and training stability. This study trains and evaluates the model on a self-built dataset with 2,237 images. The dataset covers diverse scenes in Guangzhou and Foshan and includes new fine-grained regional annotations. The experimental results show that P-YOLOv10 achieves excellent performance. Its overall mean average precision (mAP) reaches 96.5%, which is 1% higher than the baseline YOLOv10. It also achieves high accuracy on the newly added license plate region recognition task. The results of this study confirm the effectiveness of the integrated optimization strategy. They provide a more accurate and more comprehensive technical solution for intelligent traffic regulation systems.
PMID:
42337303
Bibliographic data and abstract were imported from PubMed on 24 Jun 2026.
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