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 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 Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile, based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective

MANÜEL ÖZNİTELİK ÇIKARIMI VE DERİN ÖĞRENME KULLANILARAK KUMAŞ YUMUŞAKLIĞI VE BONCUKLANMA DEĞERLERİNİN OBJEKTİF BİR ŞEKİLDE ÖLÇÜLMESİ VE SINIFLANDIRILMASI

Fabric softness is a complex tactile sensation perceived by the user even before the fabrics are worn. Softness is usually the property of surface perceived by touching or pressing a finger on the fabric surface. Fabric friction properties significantly affect the tactile sensation of the garments. The yarn used, the finishing works, and the fabric structure (weaving, knitting, etc.) affect the softness. In addition, the hardness of the water used during washing, washing movements, the amount and content of the detergent and softener used also have permanent effects on the fabric softness. Softness can be evaluated by the jury members with proven effectiveness according to the predetermined scale. Our achievement within the scope of the thesis is to eliminate the differences that may occur as a result of the subjective evaluation, which may arise from qualitative observations by basing the degree of softness evaluated qualitatively on numerical data and to obtain clearer and more precise results by adding quantitative features to the evaluation process. The methodology developed for softness assessment is also applied for another textile deterioration parameter, namely pilling, and its results are also reported.Kumaş yumuşaklığı kumaşların giyilmesinden bile önce kullanıcı tarafından algılanan karmaşık bir dokunma hissidir. Yumuşaklık genellikle kumaşın parmaklarla sıkılması veya preslenmesi ile algılanan yüzey özelliğidir. Kumaş sürtünme özellikleri, giysilerin dokunma duyumlarını büyük ölçüde etkiler. Kullanılan iplik, bitim işleri ve kumaş yapısı (dokuma, örme vb.) yumuşaklığı etkilemektedir. Bunun yanında yıkama sırasında işlem gördüğü su sertliği, yıkama hareketleri, kullanılan deterjan ve yumuşatıcının miktarı ve içeriğinden de etkilenmektedir. Görsel olarak test edilen bir diğer tekstil özelliklerinden olan yumuşaklık, etkinliği kanıtlanmış jüri üyeleri tarafından aşağıdaki skalaya göre değerlendirilebilmektedir. Tez kapsamındaki kazanımımız nitel olarak değerlendirilen yumuşaklık derecesinin, sayısal verilere dayandırılarak, nitel gözlemlerden doğabilecek görsel değerlendirme sonucu oluşacak farklılıkların giderilmesi ve değerlendirme prosesine nicel özellik kazandırarak daha net ve kesin sonuçların elde edilmesidir. Yumuşaklık için geliştirilen metodoloji değerlendirme aynı zamanda başka bir tekstil bozulma parametresi, boncuklanma için de uygulanmış ve sonuçları raporlanmıştır.M.S. - Master of Scienc

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

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