Connection Recognition Method Using Improved YoloV4 Algorithm

李翠明; 王龙; 徐龙儿; 王华

doi:10.13433/j.cnki.1003-8728.20230133

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Connection Recognition Method Using Improved YoloV4 Algorithm

更新时间：2026-07-10

- Connection Recognition Method Using Improved YoloV4 Algorithm
- Mechanical Science and Technology for Aerospace Engineering Vol. 43, Issue 12, Pages: 2138-2146(2024)
- 作者机构：
  
  兰州理工大学机构机电工程学院,兰州,730050
- 作者简介：
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20230133
  CLC：
- Published：2024
- 稿件说明：
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李翠明, 王龙, 徐龙儿, et al. Connection Recognition Method Using Improved YoloV4 Algorithm[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(12): 2138-2146.
DOI：

李翠明, 王龙, 徐龙儿, et al. Connection Recognition Method Using Improved YoloV4 Algorithm[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(12): 2138-2146. DOI： 10.13433/j.cnki.1003-8728.20230133.

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.

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