现代信息科技2024，Vol.8Issue(15)：28-35,8.DOI:10.19850/j.cnki.2096-4706.2024.15.007
基于改进YOLOv5的乒乓球轻量化网络检测模型
Lightweight Network Detection Model for Table Tennis Balls Based on Improved YOLOv5
摘要
Abstract
Ball games are the most popular sports in traditional sports competitions,and the target detection of balls can be used to improve the analysis of sports games,the security of surveillance systems,and the realism of Virtual Reality experiences.YOLOv5,as an excellent single-stage detection algorithm,is one of the most frequently used target detection algorithms in the field of computer vision in recent years due to its easy platform portability and simple detection steps.However,the YOLOv5 model has a large number of parameters.In order to reduce the number of parameters so that it can be ported to other platforms faster,this paper proposes a lightweight and improved YOLOv5 algorithm,which takes YOLOv5s as the base model,and reduces the amount of computation and improves the accuracy by the methods of replacing the backbone network with the improved MobileNetv3,introducing the CBAM Attention Mechanism in the neck,and improving the C3 module.The improved model is verified after the training,and the experimental results show that the number of parameters of the improved detection algorithm roughly decreases by 65%,and the average accuracy improves by 0.5%,which meets the accuracy requirements and real-time performance of practical application scenarios for table tennis.关键词
目标检测/YOLOv5/轻量化/CBAMKey words
target detection/YOLOv5/lightweight/CBAM分类
信息技术与安全科学引用本文复制引用
施博凯,张昕,邱天,张志鹏..基于改进YOLOv5的乒乓球轻量化网络检测模型[J].现代信息科技,2024,8(15):28-35,8.基金项目
2019年广东省拨款高校建设"冲补强"专项基金 （）
五邑大学高级人才科研启动基金2019(5041700171) （5041700171）
2021年江门市创新实践博士后课题研究资助项目(JMBSH2021B04) （JMBSH2021B04）
广东省重点领域研发计划(2020B0101030002) （2020B0101030002）
2020五邑大学大学生创新创业计划(202011349186) （202011349186）
