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

作     者：Qunyue Mu Qiancheng Yu Chengchen Zhou Lei Liu Xulong Yu 

作者机构：The College of Computer Science and EngineeringNorth Minzu UniversityYinchuan750021China The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs CommissionNorth Minzu UniversityYinchuan750021China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第80卷第7期

页      面：449-466页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the Ningxia Key Research and Development Program(Talent Introduction Special Project)Project(2022YCZX0013) North Minzu University 2022 School-Level Scientific Research Platform“Digital Agriculture Enabling Ningxia Rural Revitalization Innovation Team”(2022PT_S10) Yinchuan City University-Enterprise Joint Innovation Project(2022XQZD009) Ningxia Key Research and Development Program(Key Project)Project(2023BDE02001) 

主　　题：YOLOv8 object detection electric bicycle helmet detection electric bicycle license plate detection 

摘      要：Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic *** 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 ***,manual enforcement by traffic police is time-consuming and *** methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning *** paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these *** proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional *** 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 *** results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in ***,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.