Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Fabric defect detection systems and methods—A systematic literature review

Invited review of the book Statistical and Inductive Inference by Minimum Message Length

Deep learning–based fabric defect detection methods have been widely investigated to improve production efficiency and product quality. Although deep learning–based methods have proved to be powerf...

Mobile-Unet: An efficient convolutional neural network for fabric defect detection:

We propose a learning-based approach for automatic detection of fabric defects Our approach is based on a statistical representation of fabric patterns using the redundant contourlet transform (RCT) The distribution of the RCT coefficients are modeled using a finite mixture of generalized Gaussians (MoGG), which constitute statistical signatures distinguishing between defective and defect-free fabrics In addition to being compact and fast to compute, these signatures enable accurate localization of defects Our defect detection system is based on three main steps In the first step, a preprocessing is applied for detecting basic pattern size for image decomposition and signature calculation In the second step, labeled fabric samples are used to train a Bayes classifier (BC) to discriminate between defect-free and defective fabrics Finally, defects are detected during image inspection by testing local patches using the learned BC Our approach can deal with multiple types of textile fabrics, from simple to more complex ones Experiments on the TILDA database have demonstrated that our method yields better results compared with recent state-of-the-art methods Note to Practitioners —Fabric defect detection is central to automated visual inspection and quality control in textile manufacturing This paper deals with this problem through a learning-based approach By opposite to several existing approaches for fabric defect detection, which are effective in only some types of fabrics and/or defects, our method can deal with almost all types of patterned fabric and defects To enable both detection and localization of defects, a fabric image is first divided into local blocks, which are representative of the repetitive pattern structure of the fabric Then, statistical signatures are calculated by modeling the distribution of coefficients of an RCT using the finite MoGG The discrimination between defect-free and defective fabrics is then achieved through supervised classification of RCT-MoGG signatures based on expert-labeled examples of defective fabric images Experiments have shown that our method yields very good performance in terms of defect detection and localization In addition to its accuracy, inspection of images can be performed in a fully automatic fashion, whereas only labeled examples are initially required Finally, our method can be easily adapted to a real-time scenario since defect detection on inspected images is performed at the block level, which can be easily parallelized through hardware implementation

Automatic Fabric Defect Detection Using Learning-Based Local Textural Distributions in the Contourlet Domain

A Learning-Based Approach for Automatic Defect Detection in Textile Images

We present a unified statistical model for multivariate and multi-modal texture representation. This model is based on the formalism of finite mixtures of multivariate generalized Gaussians (MoMGG) which enables a compact and accurate representation of joint statistics of different sub-bands of multireslotion texture transform. This representation expresses correlation between sub-bands at different scales and orientations, and also between adjacent locations within the same subbands, providing a precise description of the texture layout. It enables also to combine different multi-scale transforms to build a richer and more representative texture signature. We successfully tested the model on traditional texture transforms such as wavelets and contourlets. Experiments on color-texture image retrieval have demonstrated the performance of our approach comparatively to state-of-art methods.

Daniel Yapi

Papers

Automatic Fabric Defect Detection Using Learning-Based Local Textural Distributions in the Contourlet Domain

A Learning-Based Approach for Automatic Defect Detection in Textile Images

Multi-Band Texture Modeling Using Finite Mixtures of Multivariate Generalized Gaussian Distributions

Mixture of multivariate generalized Gaussians for multi-band texture modeling and representation