Open Access

ARTICLE

Improved YOLOv8n Model for Detecting Helmets and License Plates on Electric Bicycles

Qunyue Mu^1,2, Qiancheng Yu^1,2,*, Chengchen Zhou^1,2, Lei Liu^1,2, Xulong Yu^1,2

1 The College of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China
2 The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China

* Corresponding Author: Qiancheng Yu. Email:

Computers, Materials & Continua 2024, 80(1), 449-466. https://doi.org/10.32604/cmc.2024.051728

Received 13 March 2024; Accepted 06 May 2024; Issue published 18 July 2024

Abstract

Wearing helmets while riding electric bicycles can significantly reduce head injuries resulting from traffic accidents. To effectively monitor compliance, the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles. However, manual enforcement by traffic police is time-consuming and labor-intensive. Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques. This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles, addressing these challenges. The proposed model improves upon YOLOv8n by deepening the network structure, incorporating weighted connections, and introducing lightweight convolutional modules. These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters, making it suitable for deployment on low-performance devices in real traffic scenarios. Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%, showing an 11.5% improvement over the baseline model, with a 16.2% reduction in parameters. Additionally, the model achieves a frames per second (FPS) rate of 58, meeting the accuracy and speed requirements for detection in actual traffic scenarios.

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Keywords

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