Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

Abstract

Defects in the textile manufacturing process lead to a great waste of resources and further affect the quality of textile products. Automated quality guarantee of textile fabric materials is one of the most important and demanding computer vision tasks in textile smart manufacturing. This survey presents a thorough overview of algorithms for fabric defect detection. First, this review briefly introduces the importance and inevitability of fabric defect detection towards the era of manufacturing of artificial intelligence. Second, defect detection methods are categorized into traditional algorithms and learning-based algorithms, and traditional algorithms are further categorized into statistical, structural, spectral, and model-based algorithms. The learning-based algorithms are further divided into conventional machine learning algorithms and deep learning algorithms which are very popular recently. A systematic literature review on these methods is present. Thirdly, the deployments of fabric defect detection algorithms are discussed in this study. This paper provides a reference for researchers and engineers on fabric defect detection in textile manufacturing.

Using deep learning to detect the presence/absence of defects on leather: on the way to build an industry-driven approach

“…In most of the works found in literature, validations are carried out with datasets of fabric/leather defects, such as TILDA [12] and MVTec AD [13]. A wider set of works can be consulted in recent reviews [14], [15]. In this work, which aims at the detection of defects in leather (without specific recognition, at the moment), a state-of-the-art CNN architecture -Xception [16] -was adopted to build 24 models for comparison purposes that learned from custom-made datasets variations (DSV), based on representative raw-images.…”

Automated visual inspection of fabric image using deep learning approach for defect detection

“…Most fully, the current state-of-the-approaches is presented in [5]. Some methods for visually detecting fabric defects are discussed below.…”

“…Some methods for visually detecting fabric defects are discussed below. Defect detection methods can be divided into two categories: traditional and learning-based algorithms [5]. Traditional algorithms are based on previously known functions based on spectral, structural, statistical, model approaches.…”

“…Statistical approaches use first and second-order statistics to extract texture features in texture classification [9]. Most of the approaches of this group are based on Local Binary Patterns (LBP), the spatial distribution of gray values in images [10,11], for example, Gray-Level Co-occurrence Matrices (GLCM), autocorrelation analysis, and features of fractal dimension [5]. The methods in this group compare the input image with known defect-free images, which saves computing power.…”

ClothNet: sensitive semantic segmentation network for fabric defect detection

“…6 These algorithms can be divided into four major categories: statistical algorithms, spectral algorithms, dictionary learning, and deep learning. 7 As fabric defects are random, statistical algorithms were first studied, in which the images were preprocessed and then statistically identified. A typical approach was the Sylvester matrix similarity estimation algorithm proposed by Kumari et al 8 The algorithm involved six stages: resolution matching, image enhancement using histogram specification and median mean-based sub-image-clipped histogram equalization, image registration by alignment and hysteresis processes, image subtraction, edge detection, and fault detection by rank of the Sylvester matrix.…”