A Public Fabric Database for Defect Detection Methods and Results

[EN] The use of image processing for the detection and classification of defects has been a reality for some time in science and industry. New methods are continually being presented to improve every aspect of this process. However, these new approaches are applied to a small, private collection of images, which makes a real comparative study of these methods very difficult. The objective of this paper was to compile a public annotated benchmark, that is, an extensive set of images with and without defects, and make these public, to enable the direct comparison of detection and classification methods. Moreover, different methods are reviewed and one of these is applied to the set of images; the results of which are also presented in this paper.The authors thank for the financial support provided by IVACE (Institut Valencia de Competitivitat Empresarial, Spain) and FEDER (Fondo Europeo de Desarrollo Regional, Europe), throughout the projects: AUTOVIMOTION and INTELITEX.Silvestre-Blanes, J.; Albero Albero, T.; Miralles, I.; Pérez-Llorens, R.; Moreno, J. (2019). A Public Fabric Database for Defect Detection Methods and Results. AUTEX Research Journal. 19(4):363-374. https://doi.org/10.2478/aut-2019-0035S36337419

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

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

Defect detection in textured materials using optimized filters

The problem of automated defect detection in textured materials is investigated. A new approach for defect detection using linear FIR filters with optimized energy separation is proposed. Performance of different feature separation criterion with reference to fabric defects has been evaluated. The issues relating to the design of optimal filters for supervised and unsupervised web inspection are addressed. A general web inspection system based on the optimal filters is proposed. The experiments on this new approach have yielded excellent results. The low computational requirement confirms the usefulness of the approach for industrial inspection.published_or_final_versio

Modelling visual search for surface defects

Much work has been done on developing algorithms for automated surface defect detection. However, comparisons between these models and human perception are rarely carried out. This thesis aims to investigate how well human observers can nd defects in textured surfaces, over a wide range of task di culties. Stimuli for experiments will be generated using texture synthesis methods and human search strategies will be captured by use of an eye tracker. Two di erent modelling approaches will be explored. A computational LNL-based model will be developed and compared to human performance in terms of the number of xations required to find the target. Secondly, a stochastic simulation, based on empirical distributions of saccades, will be compared to human search strategies

Textile Fingerprinting for Dismount Analysis in the Visible, Near, and Shortwave Infrared Domain

The ability to accurately and quickly locate an individual, or a dismount, is useful in a variety of situations and environments. A dismount\u27s characteristics such as their gender, height, weight, build, and ethnicity could be used as discriminating factors. Hyperspectral imaging (HSI) is widely used in efforts to identify materials based on their spectral signatures. More specifically, HSI has been used for skin and clothing classification and detection. The ability to detect textiles (clothing) provides a discriminating factor that can aid in a more comprehensive detection of dismounts. This thesis demonstrates the application of several feature selection methods (i.e., support vector machines with recursive feature reduction, fast correlation based filter) in highly dimensional data collected from a spectroradiometer. The classification of the data is accomplished with the selected features and artificial neural networks. A model for uniquely identifying (fingerprinting) textiles are designed, where color and composition are determined in order to fingerprint a specific textile. An artificial neural network is created based on the knowledge of the textile\u27s color and composition, providing a uniquely identifying fingerprinting of a textile. Results show 100% accuracy for color and composition classification, and 98% accuracy for the overall textile fingerprinting process

Mechanism of Abrasion in Nonwovens and Strategies for Abrasion Resilient Nonwovens

Fabric abrasion, especially pilling is a problem in textile industry. Pills on the fabric surface are the result of damage to the garment, which cause unappealing appearance. One of the requirements for the use of fabric in many applications is high abrasion resistance. In order to study the evolution of damage process during usage, and further investigate the relation between macro and micro mechanisms of abrasion, we performed in-situ experiments on nonwoven fabric. At macroscopic scale, different morphology of fabric have been identified when fabric rubs against a non-fiber abradant as well as against a fiber abradant. At the microscopic scale, four abrasion mechanisms at the individual fiber level have been identified. In addition, the correlation between two types of pills and six types of precursors have been found. To evaluate abrasion of nonwoven fabrics with minimal human interpretation, we apply two-dimensional, discrete-wavelet transforms to the images of nonwoven fabrics. We describe the degree of damage in terms of a gray-value ratio that is extracted from the details of the wavelet characterization, and show that this parameter correlates well with an independent qualitative assessment of the damage. In order to propose the next-generation design of fabric with better damage resistance, a fiber-level model is established using Rayleigh-Ritz and Finite-Element method based on Kirchhoff-rod theory. We have investigated the generation and evolution of perversions (an inversion of chirality) between helical segments of a fiber with uniform intrinsic curvature when the ends are restrained against rotation. The twist function k3 changes sign in passing through a perversion and this provides a convenient way to identify and approximate the morphology in more complex situations. The shape of an isolated perversion is well approximated by a simple Rayleigh-Ritz trial function. The lowest energy state is one in which perversions occur only when they are geometrically necessary because of the end restraint against rotation. However, the energy differential is small when the fiber is almost straight, so additional perversions may be introduced by noise in the early stages of unloading when the fiber is almost straight. If the fiber is further unloaded, perversion pairs may approach and annihilate each other, but if the perversions are too far from each other or from the fiber ends, an effective energy barrier exists so that they may persist well below the loading conditions where the energy differential is significant. A sufficiently rapid unloading resulted in a higher density of perversions being frozen into the fiber, than that obtained by slower rates of unloading, suggesting an analogy to the retention of defects in solids after thermal quenching.PHDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155087/1/dandanw_1.pd

Image texture analysis for inferential sensing in the process industries

Thesis (MScEng)-- Stellenbosch University, 2013.ENGLISH ABSTRACT: The measurement of key process quality variables is important for the efficient and economical operation of many chemical and mineral processing systems, as these variables can be used in process monitoring and control systems to identify and maintain optimal process conditions. However, in many engineering processes the key quality variables cannot be measured directly with standard sensors. Inferential sensing is the real-time prediction of such variables from other, measurable process variables through some form of model. In vision-based inferential sensing, visual process data in the form of images or video frames are used as input variables to the inferential sensor. This is a suitable approach when the desired process quality variable is correlated with the visual appearance of the process. The inferential sensor model is then based on analysis of the image data. Texture feature extraction is an image analysis approach by which the texture or spatial organisation of pixels in an image can be described. Two texture feature extraction methods, namely the use of grey-level co-occurrence matrices (GLCMs) and wavelet analysis, have predominated in applications of texture analysis to engineering processes. While these two baseline methods are still widely considered to be the best available texture analysis methods, several newer and more advanced methods have since been developed, which have properties that should theoretically provide these methods with some advantages over the baseline methods. Specifically, three advanced texture analysis methods have received much attention in recent machine vision literature, but have not yet been applied extensively to process engineering applications: steerable pyramids, textons and local binary patterns (LBPs). The purpose of this study was to compare the use of advanced image texture analysis methods to baseline texture analysis methods for the prediction of key process quality variables in specific process engineering applications. Three case studies, in which texture is thought to play an important role, were considered: (i) the prediction of platinum grade classes from images of platinum flotation froths, (ii) the prediction of fines fraction classes from images of coal particles on a conveyor belt, and (iii) the prediction of mean particle size classes from images of hydrocyclone underflows. Each of the five texture feature sets were used as inputs to two different classifiers (K-nearest neighbours and discriminant analysis) to predict the output variable classes for each of the three case studies mentioned above. The quality of the features extracted with each method was assessed in a structured manner, based their classification performances after the optimisation of the hyperparameters associated with each method. In the platinum froth flotation case study, steerable pyramids and LBPs significantly outperformed the GLCM, wavelet and texton methods. In the case study of coal fines fractions, the GLCM method was significantly outperformed by all four other methods. Finally, in the hydrocyclone underflow case study, steerable pyramids and LBPs significantly outperformed GLCM and wavelet methods, while the result for textons was inconclusive. Considering all of these results together, the overall conclusion was drawn that two of the three advanced texture feature extraction methods, namely steerable pyramids and LBPs, can extract feature sets of superior quality, when compared to the baseline GLCM and wavelet methods in these three case studies. The application of steerable pyramids and LBPs to further image analysis data sets is therefore recommended as a viable alternative to the traditional GLCM and wavelet texture analysis methods.AFRIKAANSE OPSOMMING: Die meting van sleutelproseskwaliteitsveranderlikes is belangrik vir die doeltreffende en ekono-miese werking van baie chemiese– en mineraalprosesseringsisteme, aangesien hierdie verander-likes gebruik kan word in prosesmonitering– en beheerstelsels om die optimale prosestoestande te identifiseer en te handhaaf. In baie ingenieursprosesse kan die sleutelproseskwaliteits-veranderlikes egter nie direk met standaard sensors gemeet word nie. Inferensiële waarneming is die intydse voorspelling van sulke veranderlikes vanaf ander, meetbare prosesveranderlikes deur van ‘n model gebruik te maak. In beeldgebaseerde inferensiële waarneming word visuele prosesdata, in die vorm van beelde of videogrepe, gebruik as insetveranderlikes vir die inferensiële sensor. Hierdie is ‘n gepaste benadering wanneer die verlangde proseskwaliteitsveranderlike met die visuele voorkoms van die proses gekorreleer is. Die inferensiële sensormodel word dan gebaseer op die analise van die beelddata. Tekstuurkenmerkekstraksie is ‘n beeldanalisebenadering waarmee die tekstuur of ruimtelike organisering van die beeldelemente beskryf kan word. Twee tekstuurkenmerkekstraksiemetodes, naamlik die gebruik van grysskaalmede-aanwesigheidsmatrikse (GSMMs) en golfie-analise, is sterk verteenwoordig in ingenieursprosestoepassings van tekstuuranalise. Alhoewel hierdie twee grondlynmetodes steeds algemeen as die beste beskikbare tekstuuranalisemetodes beskou word, is daar sedertdien verskeie nuwer en meer gevorderde metodes ontwikkel, wat beskik oor eienskappe wat teoreties voordele vir hierdie metodes teenoor die grondlynmetodes behoort te verskaf. Meer spesifiek is daar drie gevorderde tekstuuranalisemetodes wat baie aandag in onlangse masjienvisieliteratuur geniet het, maar wat nog nie baie op ingenieursprosesse toegepas is nie: stuurbare piramiedes, tekstons en lokale binêre patrone (LBPs). Die doel van hierdie studie was om die gebruik van gevorderde tekstuuranalisemetodes te vergelyk met grondlyntekstuuranaliesemetodes vir die voorspelling van sleutelproseskwaliteits-veranderlikes in spesifieke prosesingenieurstoepassings. Drie gevallestudies, waarin tekstuur ‘n belangrike rol behoort te speel, is ondersoek: (i) die voorspelling van platinumgraadklasse vanaf beelde van platinumflottasieskuime, (ii) die voorspelling van fynfraksieklasse vanaf beelde van steenkoolpartikels op ‘n vervoerband, en (iii) die voorspelling van gemiddelde partikelgrootteklasse vanaf beelde van hidrosikloon ondervloeie. Elk van die vyf tekstuurkenmerkstelle is as insette vir twee verskillende klassifiseerders (K-naaste bure en diskriminantanalise) gebruik om die klasse van die uitsetveranderlikes te voorspeel, vir elk van die drie gevallestudies hierbo genoem. Die kwaliteit van die kenmerke wat deur elke metode ge-ekstraheer is, is op ‘n gestruktureerde manier bepaal, gebaseer op hul klassifikasieprestasie na die optimering van die hiperparameters wat verbonde is aan elke metode. In die platinumskuimflottasiegevallestudie het stuurbare piramiedes en LBPs betekenisvol beter as die GSMM–, golfie– en tekstonmetodes presteer. In die steenkoolfynfraksiegevallestudie het die GSMM-metode betekenisvol slegter as al vier ander metodes presteer. Laastens, in die hidrosikloon ondervloeigevallestudie het stuurbare piramiedes en LBPs betekenisvol beter as die GSMM– en golfiemetodes presteer, terwyl die resultaat vir tekstons nie beslissend was nie. Deur al hierdie resultate gesamentlik te beskou, is die oorkoepelende gevolgtrekking gemaak dat twee van die drie gevorderde tekstuurkenmerkekstraksiemetodes, naamlik stuurbare piramiedes en LBPs, hoër kwaliteit kenmerkstelle kan ekstraheer in vergelyking met die GSMM– en golfiemetodes, vir hierdie drie gevallestudies. Die toepassing van stuurbare piramiedes en LBPs op verdere beeldanalise-datastelle word dus aanbeveel as ‘n lewensvatbare alternatief tot die tradisionele GSMM– en golfietekstuuranalisemetodes