Detecting Defects in Fabric with Laser-Based Morphological Image Processing
TL;DR: In this paper, a spatial filter is placed at the Fourier plane to remove the periodic grating structure of the fabric from the image and morphological operations with a critically selected structuring element are then applied to the image after suitable pre-processing.
Abstract: Morphological operations such as erosion and opening are applied to both direct and spatially filtered images of test fabrics to identify defects. Detecting defects morpholog ically on spatially filtered images of fabrics produces better results, particularly when the fabric is fine and contains defects of small size. The diffraction pattern of the test fabric is obtained optically by illuminating it with a collimated laser beam. A spatial filter is placed at the Fourier plane to remove the periodic grating structure of the fabric from the image. Morphological operations with a critically selected structuring element are then applied to the image after suitable pre-processing.
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TL;DR: This paper attempts to present the first survey on fabric defect detection techniques presented in about 160 references, and suggests that the combination of statistical, spectral and model-based approaches can give better results than any single approach.
Abstract: The investment in an automated fabric defect detection system is more than economical when reduction in labor cost and associated benefits are considered. The development of a fully automated web inspection system requires robust and efficient fabric defect detection algorithms. The inspection of real fabric defects is particularly challenging due to the large number of fabric defect classes, which are characterized by their vagueness and ambiguity. Numerous techniques have been developed to detect fabric defects and the purpose of this paper is to categorize and/or describe these algorithms. This paper attempts to present the first survey on fabric defect detection techniques presented in about 160 references. Categorization of fabric defect detection techniques is useful in evaluating the qualities of identified features. The characterization of real fabric surfaces using their structure and primitive set has not yet been successful. Therefore, on the basis of the nature of features from the fabric surfaces, the proposed approaches have been characterized into three categories; statistical, spectral and model-based. In order to evaluate the state-of-the-art, the limitations of several promising techniques are identified and performances are analyzed in the context of their demonstrated results and intended application. The conclusions from this paper also suggest that the combination of statistical, spectral and model-based approaches can give better results than any single approach, and is suggested for further research.
628 citations
Cites background or methods from "Detecting Defects in Fabric with La..."
...Mallik-Goswami and Datta [54] have also detected fabric defects using laser-based morphological operations....
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...However the experimental results presented in [54] are on obvious defects and do not suggest any advantage over other available less complex approaches....
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TL;DR: This paper systematically review recent advances in surface inspection using computer vision and image processing techniques, particularly those based on texture analysis methods, to review the state-of-the-art techniques for the purposes of visual inspection and decision making schemes that are able to discriminate the features extracted from normal and defective regions.
Abstract: In this paper, we systematically review recent advances in surface inspection using computer vision and image processing techniques, particularly those based on texture analysis methods. The aim is to review the state-of-the-art techniques for the purposes of visual inspection and decision making schemes that are able to discriminate the features extracted from normal and defective regions. This field is so vast that it is impossible to cover all the aspects of visual inspection. This paper focuses on a particular but important subset which generally treats visual surface inspection as texture analysis problems. Other topics related to visual inspection such as imaging system and data acquisition are out of the scope of this survey. The surface defects are loosely separated into two types. One is local textural irregularities which is the main concern for most visual surface inspection applications. The other is global deviation of colour and/or texture, where local pattern or texture does not exhibit abnormalities. We refer this type of defects as shade or tonality problem. The second type of defects have been largely neglected until recently, particularly when colour imaging system has been widely used in visual inspection and where chromatic consistency plays an important role in quality control. The emphasis of this survey though is still on detecting local abnormalities, given the fact that majority of the reported works are dealing with the first type of defects. The techniques used to inspect textural abnormalities are discussed in four categories, statistical approaches, structural approaches, filter based methods, and model based approaches, with a comprehensive list of references to some recent works. Due to rising demand and practice of colour texture analysis in application to visual inspection, those works that are dealing with colour texture analysis are discussed separately. It is also worth noting that processing vector-valued data has its unique challenges, which conventional surface inspection methods have often ignored or do not encounter. We also compare classification approaches with novelty detection approaches at the decision making stage. Classification approaches often require supervised training and usually provide better performance than novelty detection based approaches where training is only carried out on defect-free samples. However, novelty detection is relatively easier to adapt and is particularly desirable when training samples are incomplete.
461 citations
Cites background from "Detecting Defects in Fabric with La..."
...Recommended for acceptance by David Fofi and Ralph Seulin ELCVIA ISSN:1577-5097 Published by Computer Vision Center / Universitat Autònoma de Barcelona, Barcelona, Spain...
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TL;DR: A generic approach that requires small training data for ASI is proposed, which builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning network.
Abstract: Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning network. Next, pixel-wise prediction is obtained by convolving the trained classifier over input image. An experiment on three public and one industrial data set is carried out. The experiment involves two tasks: 1) image classification and 2) defect segmentation. The results of proposed algorithm are compared against several best benchmarks in literature. In the classification tasks, the proposed method improves accuracy by 0.66%–25.50%. In the segmentation tasks, the proposed method reduces error escape rates by 6.00%–19.00% in three defect types and improves accuracies by 2.29%–9.86% in all seven defect types. In addition, the proposed method achieves 0.0% error escape rate in the segmentation task of industrial data.
328 citations
Cites background from "Detecting Defects in Fabric with La..."
...applied to repetitive patterns such as textile [7], fabrics [8], and leather [9]....
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...tion [7], and morphological operations [8], [10]....
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TL;DR: This paper proposes an approach to detect and localize defects with only defect-free samples for model training by reconstructing image patches with convolutional denoising autoencoder networks at different Gaussian pyramid levels, and synthesizing detection results from these different resolution channels.
Abstract: Automated defect inspection has long been a challenging task especially in industrial applications, where collecting and labeling large amounts of defective samples are usually harsh and impracticable. In this paper, we propose an approach to detect and localize defects with only defect-free samples for model training. This approach is carried out by reconstructing image patches with convolutional denoising autoencoder networks at different Gaussian pyramid levels, and synthesizing detection results from these different resolution channels. Reconstruction residuals of the training patches are used as the indicator for direct pixelwise defect prediction, and the reconstruction residual map generated in each channel is combined to generate the final inspection result. This novel method has two prominent characteristics, which benefit the implementation of automatic defect inspection in practice. First, it is absolutely unsupervised that no human intervention is needed throughout the inspection process. Second, multimodal strategy is utilized in this method to synthesize results from multiple pyramid levels. This strategy is capable of improving the robustness and accuracy of the method. To evaluate this approach, experiments on convergence, noise immunity, and defect inspection accuracy are conducted. Furthermore, comparative tests with some excellent algorithms on actual and simulated data sets are performed. Experimental results demonstrated the effectiveness and superiority of the proposed method on homogeneous and nonregular textured surfaces.
204 citations
Cites background from "Detecting Defects in Fabric with La..."
...This approach is commonly applied to textures with repetitive patterns such as fabrics [10] and bricks [11]....
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TL;DR: This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles, and describes artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way.
Abstract: This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.
167 citations
Cites background from "Detecting Defects in Fabric with La..."
...They give an outstanding opportunity for segmenting defects and general defects detection, as reported in [56]....
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References
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Book•
11 Feb 1984
TL;DR: This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
Abstract: Image Processing and Mathematical Morphology-Frank Y. Shih 2009-03-23 In the development of digital multimedia, the importance and impact of image processing and mathematical morphology are well documented in areas ranging from automated vision detection and inspection to object recognition, image analysis and pattern recognition. Those working in these ever-evolving fields require a solid grasp of basic fundamentals, theory, and related applications—and few books can provide the unique tools for learning contained in this text. Image Processing and Mathematical Morphology: Fundamentals and Applications is a comprehensive, wide-ranging overview of morphological mechanisms and techniques and their relation to image processing. More than merely a tutorial on vital technical information, the book places this knowledge into a theoretical framework. This helps readers analyze key principles and architectures and then use the author’s novel ideas on implementation of advanced algorithms to formulate a practical and detailed plan to develop and foster their own ideas. The book: Presents the history and state-of-the-art techniques related to image morphological processing, with numerous practical examples Gives readers a clear tutorial on complex technology and other tools that rely on their intuition for a clear understanding of the subject Includes an updated bibliography and useful graphs and illustrations Examines several new algorithms in great detail so that readers can adapt them to derive their own solution approaches This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
9,566 citations
Book•
25 Sep 1992
TL;DR: This book is designed to be of interest to optical, electrical and electronics, and electro-optic engineers, including image processing, signal processing, machine vision, and computer vision engineers, applied mathematicians, image analysts and scientists and graduate-level students in image processing and mathematical morphology courses.
Abstract: Presents the statistical analysis of morphological filters and their automatic optical design, the development of morphological features for image signatures, and the design of efficient morphological algorithms. Extends the morphological paradigm to include other branches of science and mathematics.;This book is designed to be of interest to optical, electrical and electronics, and electro-optic engineers, including image processing, signal processing, machine vision, and computer vision engineers, applied mathematicians, image analysts and scientists and graduate-level students in image processing and mathematical morphology courses.
435 citations
TL;DR: In this paper, the authors used conventional image analysis hardware to image solid-shade, unpattemed, woven fabrics and two different software approaches for detecting and classifying knot and slub defects were studied.
Abstract: Conventional image analysis hardware was used to image solid-shade, unpattemed, woven fabrics Two different software approaches for detecting and classifying knot and slub defects were studied and
202 citations
TL;DR: A parallel version of the FFT for weaving has been developed that automates the very labor-intensive and therefore time-heavy and expensive process of hand-winding the fabric.
Abstract: The Fast Fourier Transform (FFT) plays a very important role in image processing and pattern recognition. Since a woven fabric consists of regular repeating units, the FFT is particularly useful fo...
194 citations
"Detecting Defects in Fabric with La..." refers background in this paper
...patterns are modulated by the existence of defects [5, 6, 9, 8]....
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TL;DR: The regular periodic nature of many textile patterns permits Fourier transform techniques in image processing to be used to measure their visual characteristics in carpets, the patterns may be due as mentioned in this paper, and the pattern may be found in many fabrics.
Abstract: The regular periodic nature of many textile patterns permits Fourier transform techniques in image processing to be used to measure their visual characteristics In carpets, the patterns may be due
188 citations