Fabric defect detection via learned dictionary-based visual saliency

Li, Chunlei; Yang, Ruimin; Liu, Zhoufeng; Gao, Guangshuai; Liu, Qiuli

doi:10.1108/ijcst-12-2015-0134

Cited by 13 publications

(15 citation statements)

References 35 publications

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“…Dictionary learning-based approaches have shown good results for fabric defect detection [3]. Li et al [32] proposed a learned dictionary-based visual saliency approach for FDD. is approach partitioned the image into blocks, and a dictionary is then constructed for both normal and defective image samples based on sorted local binary features correlation.…”

Section: Dictionary Learning-based Approachesmentioning

confidence: 99%

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Rasheed

Zafar

Rasheed³

et al. 2020

Mathematical Problems in Engineering

106

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There are different applications of computer vision and digital image processing in various applied domains and automated production process. In textile industry, fabric defect detection is considered as a challenging task as the quality and the price of any textile product are dependent on the efficiency and effectiveness of the automatic defect detection. Previously, manual human efforts are applied in textile industry to detect the defects in the fabric production process. Lack of concentration, human fatigue, and time consumption are the main drawbacks associated with the manual fabric defect detection process. Applications based on computer vision and digital image processing can address the abovementioned limitations and drawbacks. Since the last two decades, various computer vision-based applications are proposed in various research articles to address these limitations. In this review article, we aim to present a detailed study about various computer vision-based approaches with application in textile industry to detect fabric defects. The proposed study presents a detailed overview of histogram-based approaches, color-based approaches, image segmentation-based approaches, frequency domain operations, texture-based defect detection, sparse feature-based operation, image morphology operations, and recent trends of deep learning. The performance evaluation criteria for automatic fabric defect detection is also presented and discussed. The drawbacks and limitations associated with the existing published research are discussed in detail, and possible future research directions are also mentioned. This research study provides comprehensive details about computer vision and digital image processing applications to detect different types of fabric defects.

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Section: Dictionary Learning-based Approachesmentioning

confidence: 99%

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Rasheed

Zafar

Rasheed³

et al. 2020

Mathematical Problems in Engineering

106

View full text Add to dashboard Cite

show abstract

“…Liu et al developed a context-based local texture saliency detection model, which shows promising performance for fabric defect detection [20]. Li et al employed a learned dictionary to generate saliency maps for fabric defect images, and then an improved valley-emphasis method is applied to segment the defect region based on the saliency maps [21]. Guan et al proposed the integrated model of top-down and bottom-up visual attention, and the adapted threshold method is employed to detect the defect region [22].…”

Section: A Saliency Detectionmentioning

confidence: 99%

Uneven Illumination Surface Defects Inspection Based on Saliency Detection and Intrinsic Image Decomposition

Qiu

Tang

et al. 2020

IEEE Access

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Surface defect detection based on computer vision remains a challenging task due to the uneven illumination, low contrast and miscellaneous patterns of defects. Current methods usually present undesirable detection accuracy and lack adaptability for the various scenes. In the paper, the novel uneven illumination surface defects inspection (UISDI) method is proposed to address these issues. First, the multi-scale saliency detection (MSSD) method is proposed to construct a coarse defect map and obtain the corresponding background regions. Second, a novel background similarity prior-based intrinsic image decomposition model (BSIID) is applied to divide the defect image into a non-defective shading layer and a defective reflectance layer. An accelerated optimization solution is proposed to solve the minimization problem of the intrinsic image decomposition model. Last, the enhanced defect image is obtained by filtering the reflectance image and is then utilized to accurately segment the defect region from the coarse defect map. The experiments conducted using four real-world defect datasets demonstrate that the proposed method outperforms state-of-the-art methods.

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“…The traditional methods can be divided into four categories: statistical, spectral, model-based and dictionary learning method. The statistical method splits the test image into image block with the same size and extracts their statistical texture feature such as histogram (Li et al , 2019a), gray-level co-occurrence (Dash, 2018) and LBP (Li et al , 2016), and then the defective image block is identified by calculating the feature difference between the test image block with the other blocks. Therefore, the performance of these methods depends on the suitable image block size, and they cannot effectively detect the defects with small size.…”

Section: Related Workmentioning

confidence: 99%

Fabric defect detection method based on cascaded low-rank decomposition

Liu

et al. 2020

IJCST

Self Cite

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PurposeThe purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.Design/methodology/approachThis paper proposed a fabric defect detection algorithm based on cascaded low-rank decomposition. First, the constructed Gabor feature matrix is divided into a low-rank matrix and sparse matrix using low-rank decomposition technique, and the sparse matrix is used as priori matrix where higher values indicate a higher probability of abnormality. Second, we conducted the second low-rank decomposition for the constructed texton feature matrix under the guidance of the priori matrix. Finally, an improved adaptive threshold segmentation algorithm was adopted to segment the saliency map generated by the final sparse matrix to locate the defect regions.FindingsThe proposed method was evaluated on the public fabric image databases. By comparing with the ground-truth, the average detection rate of 98.26% was obtained and is superior to the state-of-the-art.Originality/valueThe cascaded low-rank decomposition was first proposed and applied into the fabric defect detection. The quantitative value shows the effectiveness of the detection method. Hence, the proposed method can be used for accurate defect detection and automated analysis system.

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Fabric defect detection via learned dictionary-based visual saliency

Cited by 13 publications

References 35 publications

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Uneven Illumination Surface Defects Inspection Based on Saliency Detection and Intrinsic Image Decomposition

Fabric defect detection method based on cascaded low-rank decomposition

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