Patterned fabric defect detection via convolutional matching pursuit dual-dictionary

Jing, Junfeng; Fan, Xiaoting; Li, Pengfei

doi:10.1117/1.oe.55.5.053109

Cited by 12 publications

(11 citation statements)

References 22 publications

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“…This method has a good effect on the defect detection of raw fabric. Jing et al [24] proposed a convolutional matching pursuit dual-dictionary algorithm for patterned fabric defect detection. Dual-dictionary and sparse coefficients of the defect-free sample set are obtained via convolutional matching pursuit and the Ksingular value decomposition based on a Gabor filter.…”

Section: Related Workmentioning

confidence: 99%

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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%

“…In the experiment, TILDA [24][28] fabric sample database is used to test our proposed algorithm. TILDA fabric sample database is a standard fabric defect sample database, which contains two groups of fabric samples C1 and C2.…”

Section: A Performance On Raw Fabric Defect Detectionmentioning

confidence: 99%

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

Kang

Zhang

2020

IEEE Access

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“…Jing et al [11] proposed a convolutional matching pursuit (CMP) dual-dictionary method for patterned fabric defect detection. A set of defect-free image blocks are selected as a sample set by sliding window.…”

Section: Related Work a Fabric Defect Detectionmentioning

confidence: 99%

“…These methods work well for simple plain and twill fabric images, but not for fabric of complex texture, and hence they can not be directly applied to patterned fabrics. The second category of methods are for fabric image with patterned texture, as shown in Fig 1(b), including Elo rating method [9], Motif-based method [10], and convolutional matching pursuit (CMP) dual-dictionary method [11], etc. These methods localize the defect using template-matching approach, requiring use of a suitable template and precise alignment.…”

Section: Introductionmentioning

confidence: 99%

Fusing Multilevel Deep Features for Fabric Defect Detection Based NTV-RPCA

Dong

Wang

et al. 2020

IEEE Access

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Fabric defect detection plays an important role in automated inspection and quality control in textile manufacturing. As the fabric images have complex and diverse textures and defects, traditional detection methods show a poor adaptability and low detection accuracy. Robust principal component analysis (RPCA) model that can be used to separate the image into object and background have proven applicable in fabric defect detection. However, how to represent texture feature of the fabric image more effectively is still problematic in this kind of method. In addition, the use of the traditional RPCA may result in low accuracy and more noises in sparse part. In this paper, a novel fabric defect detection method based on multilevel deep features fusion and non-convex total variation regularized RPCA (NTV-RPCA) is proposed. Firstly, the image representation ability is well enhanced through multilevel deep features extracted by a convolutional neural network. Then, the non-convex total variation regularized RPCA is proposed in which total variation constraint significantly reduces the noises in sparse part and non-convex solution is more approximate to the authentic one. Next, multilevel saliency maps generated by the sparse matrixes are fused via RPCA to produce a more reliable detection result. Finally, the defect region is located by segmenting the fused saliency map via a threshold segmentation algorithm. Qualitative and quantitative experiments conducted on two public fabric image databases demonstrate that the proposed method improves the adaptability and detection accuracy comparing to the state-of-the-arts.

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“…However, it is statistically shown that the accuracy of manual detection is only from 60 to 75%. Moreover, the detection rate is often influenced by many emotional factors and detection conditions [2, 3]. Therefore, in order to increase the detection rate as well as to reduce the labour cost, it is necessary to develop an automated detection technology to accomplish high‐accurate and low‐cost fabric defect detection (FDD) [4].…”

Section: Introductionmentioning

confidence: 99%