Fabric Property and Defect Detection using Deep Learning Model

Abstract

Abstract: Textile manufacturing errors waste a lot of resources and lower the quality of the end products [2]. Visual image analysis using machine learning (ML) techniques may be quite useful in identifying fabric properties and defects. One of the most essential and difficult computer vision jobs in textile smart manufacturing is the automatic fault evaluation of these fabric materials. The objective of the proposed work is to develop effective deep learning models that are trained on fabric photos for quick fabric property and defect identification Convolutional Neural Network (CNN). A modified CNN architecture is used for the fault detection procedure. If the device detects a probable fault in the cloth roll, it stops operating and alerts the adjacent operator who then decides whether to accept or reject the results of the system evaluation, provides the go-ahead for the process to proceed, and switches to the next fabric picture. These findings demonstrate a system that can operate swiftly and effectively while precisely identifying a wide range of faults.