Robust Fabric Defects Inspection System Using Deep Learning Architecture

Abstract

Abstract Clothing is one of the fundamental requirements for living. The fabric business is a steadily developing industry because the interest in dress will never diminish. To support the development of the clothing industry, the clothing industry needs to take rigid measures to keep up the quality of the pieces of fabric they produce. The industry needs a worker to screen the quality of the fabric using a manual fabric review framework. The goal of this article is to plan a profound deep learning algorithm to recognize the fabric types using computer vision. This article focuses on identification of fabric defects using convolutional neural network with the use of appropriate pooling layer, softmax layer, and rectified linear activation layer to acquire an undeniable degree of precision. The photographs of garments with various fabric defects like fabric broken pick defect, fabric with pattern, soiled fabric, fabric weft yarn defect, and plain fabric are considered for evaluation of the architecture. The performance of the architecture is measured with various performance measures like sensitivity, specificity, and accuracy. The algorithm produces the highest accuracy of 97.5 and 100 % for the training and testing samples, respectively, for soiled fabric type.