摘要
汽车零部件在实际生产过程中,打磨、抛光等各种无法确定的因素会在零部件的表面留下缺陷,从而严重影响车辆的组装、制造,存在巨大的安全隐患,因此车辆零部件的缺陷检测十分重要。零部件缺陷检测已从传统的手工分类发展为基于机器视觉的方法。本文基于卷积神经网络对VGG16网络模型进行改进,提高了模型对汽车零部件缺陷的检测精度。首先,改进了网络模型的全连接层;其次,在模型中引入了AMF-Softmax损失函数,在达到更优的聚类效果的同时解决了数据不平衡问题,最终实现了零部件缺陷的识别与定位。与传统模型的缺陷检测效果比较表明,改进的VGG16网络结构模型测试准确率可以达到97.59%,在零部件缺陷检测方面具有优越性。
In the actual production process of automobile parts,polishing,polishing and other uncertain factors will leave defects on the surface of the parts,which will seriously affect the assembly and manufacturing of vehicles,and there are huge safety risks,so the defect detection of vehicle parts is very important.Part defect detection has developed from traditional manual classification to machine vision based method.In this paper,the VGG16 network model is improved based on convolutional neural network to improve the detection accuracy of the model for automobile parts defects.Firstly,the full connection layer of the network model is improved.Secondly,AMF-Softmax loss function is introduced into the model,which solves the problem of data imbalance while achieving better clustering effect.Finally,the paper realizes the identification and location of parts defects.Compared with the traditional model,the test accuracy of the improved VGG16 network structure model can reach 97.59%,which has advantages in parts defect detection.
作者
齐金龙
张俊峰
戴贤萍
张劲松
胡陟
QI Jinlong;ZHANG Junfeng;DAI Xianping;ZHANG Jinsong;HU Zhi(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)
出处
《智能计算机与应用》
2021年第3期167-171,共5页
Intelligent Computer and Applications
