Fabric defect detection and classification using modified VGG network

Abstract

Fabrics and garments are some of the most important utilitarian items for human beings. The textile industry is rapidly growing with various fabric models and different attractive designs. To hold up the development of the textile industry, rigid measures have to be taken to check the quality of fabric during manufacturing. Thus, skilled workers are required to screen the quality of the fabric manually. This paper focuses on designing a deep learning framework to detect various fabric types and classify the defects using artificial intelligence. The proposed work has two phases; in phase 1, the input image is preprocessed with a novel Pseudo–Convolutional Neural Network (P-CNN) having zero tunable parameters. In phase 2, a modified Convolutional Neural Network (CNN) is applied to the preprocessed image to detect and classify major fabric defects. The Convolutional Neural Network (CNN) uses appropriate hidden layers to acquire an undeniable degree of accuracy for defect classification using images. The dataset consists of five different fabric defects such as broken pick defects, pattern defects, weft yarn deformity, soiled fabrics, and plain fabric defects that are considered for training and validation of the proposed architecture. The performance of the proposed network is measured using metric parameters such as sensitivity, specificity, and accuracy. The proposed technique has high accuracy for different fabric types used for testing the creation network.