A Block-Processing Approach Using Texture Analysis for Fabric Defect Detection (original) (raw)

A Survey-Defect Detection and Classification for Fabric Texture Defects in Textile Industry

Textile industry is one of the largest and oldest sectors in the India and has a formidable presence in national economy in terms of output, investment and employment. Due to increasing demand for quality fabrics it is thus important to produce the defect free high quality fabric. Visual inspection system consumes a lot of time and are error prone. The price of the fabric is reduced to 45%-65% due to presence of various defects. The purpose of this paper is to automate the detection and classification of texture defects by computerize software. The proposed method uses a statistical based approach for the inspection and detection of the defect on woven/knitted fabric collected from the textile industry. In this the images are acquired, pre-processed, restored and normalized to extract the statistical feature using computer vision. The extracted features are given an input to the artificial neural network decision tree classifier to compute the weighted factor for detecting and classifying the type of defects. An automatic defect detection system can increase the texture defect detection percentage and will reduce the fabrication and labour cost and improves the quality of the product. Keywords: Defect detection, Statistical approach, Computer vision, Decision tree classifier, neural network. Call for Papers: https://sites.google.com/site/ijcsis/

Detection of defects in fabrics using topothesy fractal dimension features

During the manufacturing of textiles, several types of defects occur in the fabrics. This paper explores the characterization of the fabric textures using the conventional approaches such as Gabor filter, Gabor wavelet and Gauss Markov random field (MRF) and the well-known method for surface roughness measurement in the mechanical engineering called topothesy. The topothesy and fractal dimension known as fractal parameters represent not only the roughness but also the affine self-similarity in fabric textures. The fabric texture features are tested on the database of four types of defective fabric samples, viz., torn fabric, oil stain, miss pick and interlacing of two webs, collected from the cloth mills of Berhampur. A comparison of the results of defect detection in fabrics indicates that the topothesy fractal dimension features outperform those of Gabor filter, Gabor wavelets and Gauss MRF.

Defect Detection of Fabrics by Grey-Level Co-Occurrence Matrix and Artificial Neural Network

Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms

The class of Textiles produced from terephthalic acid and ethylene glycol by condensation polymerization has many end-uses for example these are used as filter fabric in railway track to prevent soil erosion, in cement industry these are used in boiler department as filter fabric to prevent the fly-ash from mixing in the atmosphere. Presently, the quality checking is done by the human in the naked eye. The automation of quality check of the non-Newtonian fabric can be termed as Image Analysis or texture analysis problem. A Simulation study was carried out by the process of Image Analysis which consists of two steps the former is feature extraction and the later part is recognition. Various techniques or tools that are presently studied in research for texture feature extraction are Grey level co-occurrence matrix(GLCM), Markov Random Field, Gabor filter. A GLCM matrix with 28 Haralick features were taken as input for this chapter.

Fabric Defect Detection in Handlooms Cottage Silk Industries using Image Processing Techniques

International Journal of Computer Applications, 2012

Detection of defect on finished fabrics and their classification based on their appearance plays a vital role in inspection of both hand-woven and machine woven fabrics. Generally the defect detection process is carried out by making use of the manual effort, during which some of fabric defects are very small and undistinguishable and can be identified only by monitoring the variation in the intensity falling on the fabric. Till date, most of the fabric industries in India carry out the process of defect detection by making use of a very skilled labor. An automated system that could detect defects and identify them based on their physical appearance would naturally enhance the product quality and result in improved productivity to meet both customer demands and reduce the costs associated with off-quality. This paper focuses on developing algorithms to check if a given fabric contains any one of the defects listed out in [1] and if so, what kind of defect and the location of the defect within the analyzed area. The next sections of the paper deal with the defect detection process using Multi Resolution Combined Statistical and Spatial Frequency (MRCSF), Markov Random Field Matrix method (MRFM), Gray Level Weighted Matrix (GLWM) and Gray Level Co-occurrence Matrix (GLCM).

Texture classification of fabric defects using machine learning

International Journal of Electrical and Computer Engineering (IJECE), 2020

In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM's classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.

A comparative study of methods for defect detection in textile fabrics

2015

Fabric defect detection methods have been broadly classified into three categories; statistical methods, spectral methods and model-based methods. The performance of each method relies on the discriminative ability of texture features it uses. Each of the three categories has its own advantages and disadvantages and some researchers have recommended their combination for improved performance. In this paper, we compare the performance of three fabric defect detection methods, one from each of the three categories. The three methods are based on the grey-level co-occurrence matrices (GLCM), the undecimated discrete wavelet transform (UDWT) and the Gaussian Markov Random field models (GMRF) respectively from the statistical, spectral and model-based categories. The tests were done using the textile images from the TILDA dataset. To ensure classifier independence on the outcome of the comparison, the Euclidean distance and feed forward neural network classifiers were used for defect det...

Fabric Defect Detection using Image Processing

Global Journal of Computer Science and Technology

Fabric defect is one of the most important and serious matters of quality control in textile industry in Bangladesh. This task takes a lot of time and money. For this reason we have introduced a simple process to find defects on fabric based on edge detection. This process is mainly focused on image processing which can be integrated with fabric defect detection automation system. In this paper we have tried a new approach using the filter method with edge detection and found good results. Our algorithm can detect defected fabric area successfully. It can be also used in real-time defect detection considering light intensity, zoom, fabric width, camera resolution etc. As our algorithm mainly works on the principle of edge detection, it cannot detect defect on multicoloured or patterned fabric. It works well on single coloured fabric without any fold or edge.

A fractal image analysis system for fabric inspection based on a box-counting method

Computer Networks and ISDN Systems, 1998

Industrial vision systems must operate in real-time, produce a low false alarm rate and be flexible so as to accommodate changes in the manufacturing process easily. This work presents a system for fabric manufacturing inspection. This environment, like paper and birch wood board industries, has particular characteristics in which morphological feature extraction for automated visual inspection cannot be used. The utilization of fractal dimension is investigated for Ž discriminating defective areas. The efficiency of this approach is illustrated in textile images for defect recognition with. overall 96% accuracy. While this may sound complex, the method is in fact simple enough to be suitable for PC implementation, as demonstrated in the present work, and utilization across the Word Wide Web.

A Review on Fabric Defect Detection Techniques

2017

In the textile production, defect detection is an important factor on quality control process. The investment in automated texture defect detection becomes more economical reducing labor cost. The cost of fabric is often affected by the defects of fabrics that represent a major problem to the textile industry. Manual inspections have the problems as lack of accuracy and high time consumption where early and accurate fabric defect detection is an important phase of quality control. Therefore automate fabric inspection i.e. computer vision based inspection is required to reduce the drawbacks discussed above. Robust and efficient fabric defect detection algorithms are required to develop automated inspection techniques. From last two decades so many computer vision based methods have been proposed. This paper attempts to categorize and describe these algorithms. Categorization of fabric defect detection techniques is useful in evaluating the qualities of identified features.

Fabric Defect Detection and Identification: A Survey

2017

Automatic fabric inspection is valuable for maintenance of fabric quality. Defect inspection of fabric is a process which accomplished with human visual look-over using semi-automated way but it is labor prone and costly. To reduce time and cost wastage due to defects, the automatic inspection system for defect detection is used for this purpose. Artificial neural network, threshold segmentation, structural, statistical and model based approaches, computer vision method with the consolidation of multi-layer neural networks, are the method to identify the detect defects of fabrics. Empirical outcome spectacles that visualized approach has benefit of greatly analyzing speed, easy utilization, pleasant noise immunity and highly meeting the requirements for automatic fabric defects inspection. KeywordsANN, Computer vision, gray level co-occurrence matrices, image segmentation.