A comparative study of texture analysis algorithms in textile inspection applications

Nowadays, quality control is an important problem for fabric manufacturers. Typically these operations have been carried out by humans operators. However, this method has numerous drawbacks such as low precision, performance and effectiveness. Therefore, automatic inspection systems have increased substantially in the last decade. This work evaluates the performance of some texture measures in textile defect detection applications. For classification a method based on leaving-one-out is used. Our study has been carried out using a large database of samples to take into account a wide spectrum of fabrics and multiple defects of different nature reported by specialized works and publications. A ranking with the effectiveness of best algorithms is presented for every type of fabric. In addition, the computation time of algorithms is compared.This work is partially backed by the European Community (FEDER project)

Fabric Defect Detection with Deep Learning and False Negative Reduction

Quality control is an area of utmost importance for fabric production companies. By not detecting the defects present in the fabrics, companies are at risk of losing money and reputation with a damaged product. In a traditional system, an inspection accuracy of 60-75% is observed. In order to reduce these costs, a fast and automatic defect detection system, which can be complemented with the operator decision, is proposed in this paper. To perform the task of defect detection, a custom Convolutional Neural Network (CNN) was used in this work. To obtain a well-generalized system, in the training process, more than 50 defect types were used. Additionally, as an undetected defect (False Negative - FN) usually has a higher cost to the company than a non-defective fabric being classified as a defective one (false positive), FN reduction methods were used in the proposed system. In testing, when the system was in automatic mode, an average accuracy of 75% was attained; however, if the FN reduction method was applied, with intervention of the operator, an average of 95% accuracy can be achieved. These results demonstrate the ability of the system to detect many different types of defects with good accuracy whilst being faster and computationally simple.publishersversionpublishe

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

Garment smoothness appearance evaluation through computer vision

The measurement and evaluation of the appearance of wrinkling in textile products after domestic washing and drying is performed currently by the comparison of the fabric with the replicas. This kind of evaluation has certain drawbacks, the most significant of which are its subjectivity and its limitations when used with garments. In this paper, we present an automated wrinkling evaluation system. The system developed can process fabrics as well as any type of garment, independent of size or pattern on the material. The system allows us to label different parts of the garment. Thus, as different garment parts have different influence on human perception, this labeling enables the use of weighting, to improve the correlation with the human visual system. The system has been tested with different garments showing good performance and correlation with human perception. © The Author(s) 2012.Silvestre-Blanes, J.; Berenguer Sebastiá, JR.; Pérez Llorens, R.; Miralles, I.; Moreno Canton, J. (2012). Garment smoothness appearance evaluation through computer vision. Textile Research Journal. 82(3):299-309. doi:10.1177/0040517511424530S299309823López, F., Miguel Valiente, J., Manuel Prats, J., & Ferrer, A. (2008). Performance evaluation of soft color texture descriptors for surface grading using experimental design and logistic regression. Pattern Recognition, 41(5), 1744-1755. doi:10.1016/j.patcog.2007.09.011Villette, S. (2008). Simple imaging system to measure velocity and improve the quality of fertilizer spreading in agriculture. Journal of Electronic Imaging, 17(3), 031109. doi:10.1117/1.2956835Neri, F., & Tirronen, V. (2009). Memetic Differential Evolution Frameworks in Filter Design for Defect Detection in Paper Production. Studies in Computational Intelligence, 113-131. doi:10.1007/978-3-642-01636-3_7Carfagni, M., Furferi, R., & Governi, L. (2005). A real-time machine-vision system for monitoring the textile raising process. Computers in Industry, 56(8-9), 831-842. doi:10.1016/j.compind.2005.05.010Wang, W., Wong, Y. S., & Hong, G. S. (2005). Flank wear measurement by successive image analysis. Computers in Industry, 56(8-9), 816-830. doi:10.1016/j.compind.2005.05.009Cho, C.-S., Chung, B.-M., & Park, M.-J. (2005). Development of Real-Time Vision-Based Fabric Inspection System. IEEE Transactions on Industrial Electronics, 52(4), 1073-1079. doi:10.1109/tie.2005.851648Kawabata, S., Mori, M., & Niwa, M. (1997). An experiment on human sensory measurement and its objective measurement. International Journal of Clothing Science and Technology, 9(3), 203-206. doi:10.1108/09556229710168324Fan, J., Lu, D., Macalpine, J. M. K., & Hui, C. L. P. (1999). Objective Evaluation of Pucker in Three-Dimensional Garment Seams. Textile Research Journal, 69(7), 467-472. doi:10.1177/004051759906900701Fan, J., & Liu, F. (2000). Objective Evaluation of Garment Seams Using 3D Laser Scanning Technology. Textile Research Journal, 70(11), 1025-1030. doi:10.1177/004051750007001114Yang, X. B., & Huang, X. B. (2003). Evaluating Fabric Wrinkle Degree with a Photometric Stereo Method. Textile Research Journal, 73(5), 451-454. doi:10.1177/004051750307300513Kang, T. J., Kim, S. C., Sul, I. H., Youn, J. R., & Chung, K. (2005). Fabric Surface Roughness Evaluation Using Wavelet-Fractal Method. Textile Research Journal, 75(11), 751-760. doi:10.1177/0040517505058855Mohri, M., Ravandi, S. A. H., & Youssefi, M. (2005). Objective evaluation of wrinkled fabric using radon transform. Journal of the Textile Institute, 96(6), 365-370. doi:10.1533/joti.2004.0066Zaouali, R., Msahli, S., El Abed, B., & Sakli, F. (2007). Objective evaluation of multidirectional fabric wrinkling using image analysis. Journal of the Textile Institute, 98(5), 443-451. doi:10.1080/00405000701489156Yu, W., Yao, M., & Xu, B. (2009). 3-D Surface Reconstruction and Evaluation of Wrinkled Fabrics by Stereo Vision. Textile Research Journal, 79(1), 36-46. doi:10.1177/004051750809049

Advanced fibre reinforced material : non-crimp composites

Abstract: Non-crimp fabric (NCF) composites combine the superior in-plane properties of unidirectional pre-impregnated tape (UDPT) and excellent out-of-plane properties of woven fabrics without their associated drawbacks of high manufacturing cost and crimping respectively. Research on such novel composite materials have mostly been parochial and focused on improving either the matrix or the reinforcement. The aim of this thesis is therefore to present a holistic and multifaceted study (in a life cycle vision of the composite) addressing the critical factors of matrix modification, dispersion quantification, testing optimisation and fibre reclamation from waste...D.Phil. (Mechanical Engineering

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

Analysis of Basis Weight Uniformity Indexes for the Evaluation of Fiber Injection Molded Nonwoven Preforms

Fiber injection molding is an innovative approach for the manufacturing of nonwoven preforms but products currently lack a homogeneous fiber distribution. Based on a mold-integrated monitoring system, the uniformity of the manufactured preforms will be investigated. As no universally accepted definition or method for measuring uniformity is accepted yet, this article aims to find a suitable uniformity index for evaluating fiber injection molded nonwovens. Based on a literature review, different methods are implemented and used to analyze simulated images with given distribution properties, as well as images of real nonwovens. This study showed that quadrant-based methods are suitable for evaluating the basis weight uniformity. It has been found that the indexes are influenced by the number of quadrants. Changes in sample size do not affect the indexes when keeping the quadrant number constant. The quadrants-based calculation of the coefficient of variation showed the best suitability as it shows good robustness and steady index for varying degrees of fiber distribution