ResNet50: Automated Fabric Defect Detection and Classification based on a Deep Learning Approach

Abstract

"The demand for an intelligent computer-based system for visual inspection is on the rise in the textile business, especially among those who place a premium on textile quality. In this research, we present a fully automated AI-driven algorithm for defect detection in fabric, one that makes use of deep neural network models that have already been pre-trained. In order to train the networks, the fabric images go through a series of pre-processing steps where typical image processing techniques are applied. In order to train and categorize various fabric flaws, we use Deep Convolutional Neural Networks (DCNNs) and the pre-trained ResNeT network. The system obtains a best classification accuracy of 96.40% in simulations utilizing preexisting textile datasets. This model's detection and classification system can help human operators spot flaws in the fabric manufacturing process with this degree of precision."