纺织工程学报2024，Vol.2Issue(6)：55-65,11.
基于深度学习的纺织物缺陷检测技术研究
Research on detection technology of textile defects based on deep learning
摘要
Abstract
The traditional approach to textile defect detection relies on manual labour,which is subject to a num-ber of limitations in terms of its reliability,efficiency and accuracy.The application of deep learning techniques in automated textile defect detection is discussed,with a particular focus on the use of MLP,CNN,VGG16,and ResNet50 models.The performance of these models on specific datasets was evaluated through a series of met-rics,including accuracy,training loss,confusion matrix,receiver operating characteristic(ROC)curve,and t-dis-tributed stochastic neighbor embedding(t-SNE)clustering effect.The results demonstrate that the ResNet50 model exhibits optimal detection performance on the test set,with an accuracy of 96.0%,an AUC value of over 0.91,a Silhouette Score value of 0.7731,and a Davies-Bouldin Index value of 0.3195.Furthermore,a hierarchi-cal classification method was explored for hundreds of defect types present in the actual production process.The accuracy of defect detection was enhanced to 85.4%through the implementation of a tree-structured classi-fication strategy.Effective technical support is provided to facilitate the automation of textile quality inspection.关键词
深度学习/纺织物/缺陷检测/图像分类/卷积神经网络Key words
deep learning/textile material/defect detection/image classification/convolutional neural network分类
轻工纺织引用本文复制引用
杜焱铭,袁子厚,郑兴任,张红伟..基于深度学习的纺织物缺陷检测技术研究[J].纺织工程学报,2024,2(6):55-65,11.基金项目
湖北省数字化纺织装备重点实验室开放基金项目(DTL2019019). （DTL2019019）
