Defect Detection for Patterned Fabric Images Based on GHOG and Low-Rank Decomposition

In contrast to defect-free fabric images with macro-homogeneous textures and regular patterns, the fabric images with the defect are characterized by the defect regions that are salient and sparse among the redundant background. Therefore, as an effective tool for separating an image into a redundant part (the background) and sparse part (the defect), the low-rank decomposition model provides an ideal solution for patterned fabric defect detection. In this paper, a novel patterned method for fabric defect detection is proposed based on a novel texture descriptor and the low-rank decomposition model. First, an efficient second-order orientation-aware descriptor, denoted as GHOG, is designed by combining Gabor and histogram of oriented gradient (HOG). In addition, a spatial pooling strategy based on human vision mechanism is utilized to further improve the discrimination ability of the proposed descriptor. The proposed texture descriptor can make the defect-free image blocks lay in a low-rank subspace, while the defective image blocks have deviated from this subspace. Then, a constructed low-rank decomposition model divides the feature matrix generated from all the image blocks into a low-rank part, which represents the defect-free background, and a sparse part, which represents sparse defects. In addition, a non-convex log det as a smooth surrogate function is utilized to improve the efficiency of the constructed low-rank model. Finally, the defects are localized by segmenting the saliency map generated by the sparse matrix. The qualitative results and quantitative evaluation results demonstrate that the proposed method improves the detection accuracy and self-adaptivity comparing with the state-of-the-art methods

Exploratory Data Analysis for Textile Defect Detection

The capacity to recognize anomalies in real-world visual data is essential for many computer vision uses. New approaches and ideas in unsupervised defective garments identification require data for training and evaluation. Understanding the constraints of the currently employed approach of human inspection is crucial for improving clothing quality. Uses for digital image processing in the textile sector are suggested. This method proposes a novel quantitative measuring strategy by fusing digital image processing with the Lab view platform. As this study progresses, it becomes clear that the FLDA yields the best results, with 95% accuracy, while the Hoeffiding Tree yields the lowest results, with 60% accuracy. When compared to other models, the FLDA's precision of 0.96 is the best you'll find, while the Hoeffiding Tree's is the lowest at 0.62. The FLDA provides the best result, with a recall value of 0.95, while the Hoeffiding Tree shows the lowest result, with a recall value of 0.60. The FLDA yields the best results (0.90 kappa value), whereas the Hoeffiding Tree yields the worst (0.20 kappa value).The FLDA exhibits the best results, with an F-Measure value of 0.95, while the Hoeffiding Tree displays the lowest results, with an F-Measure value of 0.58. The FLDA provides the best results, with an MCC value of 0.91, while the Hoeffiding Tree displays the worst results, with an MCC value of 0.22. The FLDA yields the best results (0.98 ROC value), whereas the Decision Table produces the worst results (0.69 ROC value). The best prediction accuracy is shown by the FLDA, at 0.98 of the PRC value, while the worst is shown by the Decision Table, at 0.67. The MAE is lowest (0.07) for the FLDA and highest (0.39) for the Hoeffiding Tree. The MAE deviation of the Bayes Net is 0.19.  The best result is shown by the FLDA, with an RMSE of 0.22, while the largest RMSE deviation is found in the Hoeffiding Tree, at 0.62. The RMSEdeviation for Bayes Net is 0.41. The finest RAE is shown by the FLDA, at 13.39%, while the largest RAE deviation is 78.28% for the Hoeffiding Tree. The Bayes Net explains 38.74% of the variation in RAE.  The best result is shown by the FLDA, with an RRSE of 44.36%; the largest RRSE variation is shown by the Hoeffiding Tree, with 123.99%. When compared to other models, the IBK's preparation time of 0 seconds is by far the shortest. While the Bayes Net completes its task in 0.03 seconds, FLDA can take up to 0.17 seconds. The FLDA model is found to have superior performance in this study

Review of Application of Artificial Neural Networks in Textiles and Clothing Industries over Last Decades

2010-2011 > Academic research: refereed > Chapter in an edited book (author

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

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 204

This bibliography lists 140 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

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

Deep CNN-Based Automated Optical Inspection for Aerospace Components

ABSTRACT The defect detection problem is of outmost importance in high-tech industries such as aerospace manufacturing and is widely employed using automated industrial quality control systems. In the aerospace manufacturing industry, composite materials are extensively applied as structural components in civilian and military aircraft. To ensure the quality of the product and high reliability, manual inspection and traditional automatic optical inspection have been employed to identify the defects throughout production and maintenance. These inspection techniques have several limitations such as tedious, time- consuming, inconsistent, subjective, labor intensive, expensive, etc. To make the operation effective and efficient, modern automated optical inspection needs to be preferred. In this dissertation work, automatic defect detection techniques are tested on three levels using a novel aerospace composite materials image dataset (ACMID). First, classical machine learning models, namely, Support Vector Machine and Random Forest, are employed for both datasets. Second, deep CNN-based models, such as improved ResNet50 and MobileNetV2 architectures are trained on ACMID datasets. Third, an efficient defect detection technique that combines the features of deep learning and classical machine learning model is proposed for ACMID dataset. To assess the aerospace composite components, all the models are trained and tested on ACMID datasets with distinct sizes. In addition, this work investigates the scenario when defective and non-defective samples are scarce and imbalanced. To overcome the problems of imbalanced and scarce datasets, oversampling techniques and data augmentation using improved deep convolutional generative adversarial networks (DCGAN) are considered. Furthermore, the proposed models are also validated using one of the benchmark steel surface defects (SSD) dataset

Microgenesis, immediate experience and visual processes in reading

The concept of microgenesis refers to the development on a brief present-time scale of a percept, a thought, an object of imagination, or an expression. It defines the occurrence of immediate experience as dynamic unfolding and differentiation in which the ‘germ’ of the final experience is already embodied in the early stages of its development. Immediate experience typically concerns the focal experience of an object that is thematized as a ‘figure’ in the global field of consciousness; this can involve a percept, thought, object of imagination, or expression (verbal and/or gestural). Yet, whatever its modality or content, focal experience is postulated to develop and stabilize through dynamic differentiation and unfolding. Such a microgenetic description of immediate experience substantiates a phenomenological and genetic theory of cognition where any process of perception, thought, expression or imagination is primarily a process of genetic differentiation and development, rather than one of detection (of a stimulus array or information), transformation, and integration (of multiple primitive components) as theories of cognitivist kind have contended. My purpose in this essay is to provide an overview of the main constructs of microgenetic theory, to outline its potential avenues of future development in the field of cognitive science, and to illustrate an application of the theory to research, using visual processes in reading as an example