Passat车顶铆钉拉铆数量机器视觉检测系统  

The Visual Inspection System for Quantity of Passat Roof Rivet

作　　者：邵奇栋 尤勇 周万 SHAO Qidong;YOU Yong;ZHOU Wan(SAIC Volkswagen Nanjing Branch Company,Nanjing 211100,China)

机构地区：[1]上汽大众汽车有限公司南京分公司,江苏南京211100

出　　处：《汽车实用技术》2023年第18期140-143,共4页Automobile Applied Technology

摘　　要：在Passat车顶拉铆生产过程中,目前采用人工目视的方法检查是否存在铆钉遗漏问题,该方法检查效率较低且存在错检漏检等问题。文章将深度学习卷积神经网络引入到车顶拉铆检测中,提出了一种基于ReLU激活函数的卷积神经网络算法,并通过实验表明该算法具有精度高、鲁棒性强等特点,为深度学习应用于车身视觉检测提供了一种新的思路。实验检测成功率可以达到99.87%,满足生产过程中的质量认定要求,对工程应用推广具有指导意义。In the current riveting production process of Passat roof,manual visual inspection is used to check whether there is rivet omission,which is inefficient,and there are problems such as wrong inspection and missing inspection.In this paper,the deep learning convolution neural network is introduced into the roof riveting detection,and a convolution neural network algorithm based on ReLU activation function is proposed.Experiments show that the algorithm has high accuracy and strong robustness,which provides a new idea for the application of deep learning to vehicle body visual detection,The success rate of experimental detection reaches 99.87%,meet the quality assurance requirements in the production process,which has guiding significance for the engineering application and promotion.

关 键 词：汽车制造 视觉识别 深度学习 神经网络 

分 类 号：U463[机械工程—车辆工程]