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

GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures

Chi-square histogram distance is one of the distance measures that can be used to find dissimilarity between two histograms. Motivated by the fact that texture discrimination by human vision system is based on second-order statistics, we make use of histogram of gray-level co-occurrence matrix (GLCM) that is based on second-order statistics and propose a new machine vision algorithm for automatic defect detection on patterned textures. Input defective images are split into several periodic blocks and GLCMs are computed after quantizing the gray levels from 0-255 to 0-63 to keep the size of GLCM compact and to reduce computation time. Dissimilarity matrix derived from chi-square distances of the GLCMs is subjected to hierarchical clustering to automatically identify defective and defect-free blocks. Effectiveness of the proposed method is demonstrated through experiments on defective real-fabric images of 2 major wallpaper groups (pmm and p4m groups).Comment: IJCVR, Vol. 2, No. 4, 2011, pp. 302-31

Robust Defect Detection in Plain and Twill Fabric Using Directional Bollinger Bands

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Similarity Measures for Automatic Defect Detection on Patterned Textures

Similarity measures are widely used in various applications such as information retrieval, image and object recognition, text retrieval, and web data search. In this paper, we propose similarity-based methods for defect detection on patterned textures using five different similarity measures, viz., Normalized Histogram Intersection Coefficient, Bhattacharyya Coefficient, Pearson Product-moment Correlation Coefficient, Jaccard Coefficient and Cosine-angle Coefficient. Periodic blocks are extracted from each input defective image and similarity matrix is obtained based on the similarity coefficient of histogram of each periodic block with respect to itself and other all periodic blocks. Each similarity matrix is transformed into dissimilarity matrix containing true-distance metrics and Ward’s hierarchical clustering is performed to discern between defective and defect-free blocks. Performance of the proposed method is evaluated for each similarity measure based on precision, recall and accuracy for various real fabric images with defects such as broken end, hole, thin bar, thick bar, netting multiple, knot, and missing pick

Fabric defect segmentation using multichannel blob detectors

The problem of automated defect detection in textured materials is investigated. A new algorithm based on multichannel filtering is presented. The texture features are extracted by filtering the acquired image using a filter bank consisting of a number of real Gabor functions, with multiple narrow spatial frequency and orientation channels. For each image, we propose the use of image fusion to multiplex the information from sixteen different channels obtained in four orientations. Adaptive degrees of thresholding and the associated effect on sensitivity to material impurities are discussed. This algorithm realizes large computational savings over the previous approaches and enables high-quality real-time defect detection. The performance of this algorithm has been tested thoroughly on real fabric defects, and experimental results have confirmed the usefulness of the approach.published_or_final_versio

A Hybrid Neural Network Architecture for Texture Analysis in Digital Image Processing Applications

A new hybrid neural network model capable of texture analysis in a digital image processing environment is presented in this thesis. This model is constructed from two different types of neural network, self-organisation and back-propagation. Along with a brief resume of digital image processing concepts, an introduction to neural networks is provided. This contains appropriate documentation of the neural networks and test evidence is also presented to highlight the relative strengths and weaknesses of both neural networks. The hybrid neural network is proposed from this evidence along with methods of training and operation. This is supported by practical examples of the system's operation with digital images. Through this process two modes of operation are explored, classification and segmentation of texture content within images. Some common methods of texture analysis are also documented, with spatial grey level dependence matrices being chosen to act as a feature generator for classification by a back-propagation neural network, this provides a benchmark to assess the performance of the hybrid neural network. This takes the form of descriptive comparison, pictorial results, and mathematical analysis when using aerial survey images. Other novel approaches using the hybrid neural network are presented with concluding comments outlining the findings presented within this thesis