采用改进YoloV4算法的连接件识别方法
Connection Recognition Method Using Improved YoloV4 Algorithm
李翠明 1王龙 1徐龙儿 1王华1
作者信息
- 1. 兰州理工大学机电工程学院,兰州 730050
- 折叠
摘要
为实现连接件的自动化装配与分拣,提出了一种改进的YoloV4算法用于连接件的识别.首先在YoloV4的基础上,将YoloV4中的主干网络CSP-Darknet53替换为轻量级的GhostNet网络,同时把YoloV4中用到的普通卷积替换成深度可分离卷积来进一步减少参数量,并通过K-means++聚类算法来避免K-means聚类算法中的缺点,生成先验框尺寸.试验结果表明,改进后的YoloV4算法的平均精度值高达100％,识别速度得到大幅提高,参数量较YoloV4减少了 82％,可提高在嵌入式设备的应用范围,为智能制造提供了技术支持.
Abstract
In order to realize the automatic assembly and sorting of connectors,this paper proposes an improved YoloV4 algorithm for connector identification.First,CSP-Darknet53,the backbone network in YoloV4is replacedby a lightweight GhostNet network.At the same time,the ordinary convolution used in YoloV4is also replaced with a deeply separable convolution to further reduce the number of parameters,and K-means++clustering algorithm is used to avoid the shortcomings of K-means clustering algorithm and generate a priori box size.The experimental results show that the average accuracy of the improved YoloV4 algorithm is as high as 100％,the recognition speed is greatly improved,and the number of parameters is reduced by 82％compared with YoloV4,which can improve the application range of embedded devices and provide technical support for intelligent manufacturing.
关键词
目标识别/工业零件/YoLoV4/GhostNetKey words
target recognition/industrial parts/YoloV4/GhostNet引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据