计算机与数字工程2023，Vol.51Issue(10)：2413-2417,5.DOI:10.3969/j.issn.1672-9722.2023.10.037
FDAT:基于AlexNet迁移学习的纺织物疵点分类方法
FDAT:Fabric Defect Classification Model Based on AlexNet Using Transfer Learning
摘要
Abstract
For the problems of the existing textile product defect classification methods such as small data sets,long network training time and low accuracy,this paper proposes the fabric defect classification model based on AlexNet using transfer learning(short of FDAT).First of all,for the problem of the small amount of data in the textile product defect data set,the model training pa-rameter weights are obtained through training based on large data sets,and the transfer learning method is used to construct a textile product defect classification method based on AlexNet.Then,perform feature extraction on the input fabric defect data and use the softmax classifier to classify the feature extraction results.Finally,a computer simulation experiment is carried out on the TILDA fabric defect data set.The experimental results show that the proposed FDAT can effectively solve the small sample classification problem and improve the algorithm's performance compared with the traditional wavelet transform algorithm,artificial neural net-work,DenseNet,ResNet and Xception.It can improve the accuracy of the algorithm and shorten the time-consuming network clas-sification.关键词
图像识别/分类/疵点检测/迁移学习/AlexNetKey words
image recognition/classification/defect detection/transfer learning/AlexNet分类
信息技术与安全科学引用本文复制引用
冯一凡,师昕,赵雪青..FDAT:基于AlexNet迁移学习的纺织物疵点分类方法[J].计算机与数字工程,2023,51(10):2413-2417,5.基金项目
陕西省教育厅自然科学一般专项科学研究计划(编号:21JK0646)资助. （编号:21JK0646）
