A VISION-BASED QUALITY INSPECTION SYSTEM FOR FABRIC DEFECT DETECTION AND CLASSIFICATION

Published ThesisQuality inspection of textile products is an important issue for fabric manufacturers. It is desirable to produce the highest quality goods in the shortest amount of time possible. Fabric faults or defects are responsible for nearly 85% of the defects found by the garment industry. Manufacturers recover only 45 to 65% of their profits from second or off-quality goods. There is a need for reliable automated woven fabric inspection methods in the textile industry. Numerous methods have been proposed for detecting defects in textile. The methods are generally grouped into three main categories according to the techniques they use for texture feature extraction, namely statistical approaches, spectral approaches and model-based approaches. In this thesis, we study one method from each category and propose their combinations in order to get improved fabric defect detection and classification accuracy. The three chosen methods are the grey level co-occurrence matrix (GLCM) from the statistical category, the wavelet transform from the spectral category and the Markov random field (MRF) from the model-based category. We identify the most effective texture features for each of those methods and for different fabric types in order to combine them. Using GLCM, we identify the optimal number of features, the optimal quantisation level of the original image and the optimal intersample distance to use. We identify the optimal GLCM features for different types of fabrics and for three different classifiers. Using the wavelet transform, we compare the defect detection and classification performance of features derived from the undecimated discrete wavelet and those derived from the dual-tree complex wavelet transform. We identify the best features for different types of fabrics. Using the Markov random field, we study the performance for fabric defect detection and classification of features derived from different models of Gaussian Markov random fields of order from 1 through 9. For each fabric type we identify the best model order. Finally, we propose three combination schemes of the best features identified from the three methods and study their fabric detection and classification performance. They lead generally to improved performance as compared to the individual methods, but two of them need further improvement

Fabric inspection based on the Elo rating method

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Optimal morphological filter design for fabric defect detection

This paper investigates the problem of automated defect detection for textile fabrics and proposes a new optimal morphological filter design method for solving this problem. Gabor Wavelet Network (GWN) is adopted as a major technique to extract the texture features of textile fabrics. An optimal morphological filter can be constructed based on the texture features extracted. In view of this optimal filter, a new semi-supervised segmentation algorithm is then proposed. The performance of the scheme is evaluated by using a variety of homogeneous textile images with different types of common defects. The test results exhibit accurate defect detection with low false alarm, thus confirming the robustness and effectiveness of the proposed scheme. In addition, it can be shown that the algorithm proposed in this paper is suitable for on-line applications. Indeed, the proposed algorithm is a low cost PC based solution to the problem of defect detection for textile fabrics. © 2005 IEEE.published_or_final_versio

An Approach for Feature extraction of Fabric using wavelet transform & defect detection using Genetic algorithm

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Regularity analysis for patterned texture inspection

This paper considers regularity analysis for patterned texture material inspection. Patterned texture-like fabric is built on a repetitive unit of a pattern. Regularity is one of the most important features in many textures. In this paper, a new patterned texture inspection approach called the regular bands (RB) method is described. First, the properties of textures and the meaning of regularity measurements are presented. Next, traditional regularity analysis for patterned textures is introduced. Many traditional approaches such as co-occurrence matrices, autocorrelation, traditional image subtraction and hash function are based on the concept of periodicity. These approaches have been applied for image retrieval, image synthesis, and defect detection of patterned textures. In this paper, a new measure of periodicity for patterned textures is described. The Regular Bands method is based on the idea of periodicity. A detailed description of the RB method with definitions, procedures, and explanations is given. There is also a detailed evaluation using the Regular Bands of some patterned textures. Three kinds of patterned fabric samples are used in the evaluation and a high detection success rate is achieved. Finally, there is a discussion of the method and some conclusions. © 2006 IEEE.published_or_final_versio

A Contrast-Based Approach to the Identification of Texture Faults

Texture analysis based on the extraction of contrast features is very effective in terms of both computational complexity and discrimination capability. In this framework, max-min approaches have been proposed in the past as a simple and powerful tool to characterize a statistical texture. In the present work, a method is proposed that allows exploiting the potential of max -min approaches to efficiently solve the problem of detecting local alterations in a uniform statistical texture. Experimental results show a high defect discrimination capability and a good attitude to real-time applications, which make it particularly attractive for the development of industrial visual inspection systems

An Extended Review on Fabric Defects and Its Detection Techniques

In Textile Industry, Quality of the Fabric is the main important factor. At the initial stage, it is very essential to identify and avoid the fabrics faults/defects and hence human perception consumes lot of time and cost to reveal the fabrics faults. Now-a-days Automated Inspection Systems are very useful to decrease the fault prediction time and gives best visualizing clarity- based on computer vision and image processing techniques. This paper made an extended review about the quality parameters in the fiber-to-fabric process, fabrics defects detection terminologies applied on major three clusters of fabric defects knitting, woven and sewing fabric defects. And this paper also explains about the statistical performance measures which are used to analyze the defect detection process. Also, comparison among the methods proposed in the field of fabric defect detection

Texture analysis and Its applications in biomedical imaging: a survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021; date of current version January 24, 2022. This work was supported in part by the Portuguese Foundation for Science and Technology (FCT) under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio