Textile Defect Classification Based on Convolutional Neural Network and SVM

Abstract

Textile defect detection and classification is an important part of the textile production process, however, to detect accurately and efficiently is still difficult. In this study, we present an effective formulation for textile defect detection. Unlike traditional textile detecting methods, a conventional neural network (CNN) support vector machine (SVM) is designed to extract the depth features of textile images and to classify defects. The effectiveness of textile feature extraction is improved by optimizing Alexnet to obtain a CNN with better feature extraction and less computation. Textile defect classification is more effective using SVM instead of Softmax. Experimental results demonstrated that the improved Alexnet-SVM gave 99% accuracy in defect classification using the TILDA database. This accuracy was 5% greater than Alexnet and GoogleNet.

Weft-knitted fabric defect classification based on a Swin transformer deformable convolutional network

“…Qiu et al. 19 proposed a variant of AlexNet that could extract more valuable features and employed a support vector machine (SVM) classifier to improve precision. Zhao et al.…”

“…DenseNet, Inceptionv3 and Xception were integrated into an ensemble learning model, 18 and the classification accuracy on the Irish Longitudinal Study on Ageing (TILDA) database was 97.8%. Qiu et al 19 proposed a variant of AlexNet that could extract more valuable features and employed a support vector machine (SVM) classifier to improve precision. Zhao et al 20 also proposed an integrated CNN model based on visual long shortterm memory, and this approach could solve the dilemma of multiple indistinguishable defects.…”