Defect detection on the fabric with complex texture via dual-scale over-complete dictionary

Qu, Tao; Zou, Lian; Zhang, Qinglin; Chen, Xi; Fan, Cien

doi:10.1080/00405000.2015.1061760

Cited by 34 publications

(13 citation statements)

References 19 publications

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“…The algorithm is also not very effective at detecting defects in inadequate anomalies and some linear defects. Qu et al [28] used double-scale over-complete dictionaries to learn the characteristics of defect-free fabrics, to enhance the selfadaptability of defect detection, and then used fusion algorithms of different scales to improve the accuracy of detection. Through the experiment of raw fabric defect detection, the algorithm has achieved an excellent detection effect.…”

Section: Related Workmentioning

confidence: 99%

A Universal and Adaptive Fabric Defect Detection Algorithm Based on Sparse Dictionary Learning

Kang

Zhang

2020

IEEE Access

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Due to the complex diversity of both fabric texture and defect, fabric defect detection is a challenge topic. However, most of existing defect detection methods can still detect only one type of fabric defects. In order to solve this problem, we propose a universal and adaptive defect detection algorithm based on dictionary learning for detecting various defects of different fabric texture. Firstly, in order to make the image more balanced and improve the detection accuracy, according to the complexity of fabric texture and the degree of brightness and darkness of the background, we segment defect-free image according to a certain size to obtain the image joint matrix. In order to make the algorithm more universal, next we form a random dictionary instead of over-complete dictionary and fixed dictionary by randomly select feature columns from the image joint matrix, which can extract the most important texture and background information of fabric. Then, we use the Orthogonal Matching Pursuit algorithm to obtain the sparse representation of defect-free image. On this basis, we use the K-Singular Value Decomposition algorithm to update the dictionary iteratively to get the final dictionary. Afterward, the defective image is reconstructed with the learned dictionary to obtain the reconstruction error. Finally, the correction coefficient is obtained through reconstruction error analysis, then the corresponding adaptive threshold is used to detect and mark the location of the defects. The performance of the algorithm is evaluated on the fabric database of raw fabric, yarn-dyed fabric and patterned fabric. The experimental results show that the proposed algorithm has good universality, adaptability and a high detection success rate for different types of fabrics. Particularly, the average detection success rate of dark-red fabric and dot-patterned fabric can reach 100%, and the Precision and the Recall of defect detection evaluation index can also reach 100%.

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Section: Related Workmentioning

confidence: 99%

A Universal and Adaptive Fabric Defect Detection Algorithm Based on Sparse Dictionary Learning

Kang

Zhang

2020

IEEE Access

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“…In the past two decades, extensive efforts have been devoted for more efficient and accurate defect segmentation methods. [3][4][5][6] Traditional segmentation methods of surface defect can be mainly divided into three categories: statistical-based methods, 7,8) filter-based methods [9][10][11] and model- ISIJ International, Advance Publication by J-Stage ISIJ International, Advance Publication by J-STAGE ISIJ International, Advance Publication by J-Stage ISIJ International, J-Stage Advanced Publication, DOI: http://dx.doi.org/10.2355/isijinternational.ISIJINT-2015-@@@ ISIJ International, Advance Publication by J-STAGE, DOI: 10.2355/isijinternational.ISIJINT-2021-024 based methods. As different types of defect have different shape, size, gray, texture and location, above-mentioned approaches are basically customized for a predefined or specific type of defect, which often lacks the capacity to learn the common features of all types of defect.…”

Section: Introductionmentioning

confidence: 99%

Double Low-rank Based Matrix Decomposition for Surface Defect Segmentation of Steel Sheet

Zhou

Cui

et al. 2021

ISIJ Int.

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Despite advances in surface defect segmentation of steel sheet, it is still far from meeting the needs of real-world applications due to some method usually lack of adaptiveness to different shape, size, location and texture of defect object. Based on the assumption that each defect image is composed of defect-free background components that reflect the similarities of different regions and defect foreground components that reflect unique object information, we formulate the segmentation task as an image decomposition problem. To this end, we develop a double low-rank based matrix factorization framework for decomposing the surface defect image into defect foreground image and defect-free background image. Furthermore, considering the similarity of the defect-free background sub-regions and the defective sub-regions, Laplacian and sparse regularization terms are introduced into the matrix decomposition framework to improve their representation ability and discriminative ability. Importantly, the proposed method is unsupervised and training-free, so it does not requiring a large number of training samples with time-consuming manual labels. Experimental results on synthetic and real-world surface defect images show that the proposed method outperforms some state-of-the-art approaches in terms of both subjective and objective experiments.

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“…However, similar spectral approaches are usually computationally demanding. Qu et al [ 15 ] proposed a defect detection algorithm for fabrics with complex textures based on a dual-scale over-complete dictionary. This method can enhance the self-adaptability of defect detection by considering large variations in the defect sizes.…”

Section: Introductionmentioning

confidence: 99%

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Mei

Wang

Wen

2018

Sensors

231

121

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Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

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Defect detection on the fabric with complex texture via dual-scale over-complete dictionary

Cited by 34 publications

References 19 publications

A Universal and Adaptive Fabric Defect Detection Algorithm Based on Sparse Dictionary Learning

A Universal and Adaptive Fabric Defect Detection Algorithm Based on Sparse Dictionary Learning

Double Low-rank Based Matrix Decomposition for Surface Defect Segmentation of Steel Sheet

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

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