Detection of chemical fabric defects on the basis of morphological processing

Hu, Jiajuan; He, Zhiyuan; Weng, Guirong; Sun, Lining; Zuo, Baoqi; Wang, Chen

doi:10.1080/00405000.2015.1022094

Cited by 18 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In structural methods, defect feature is characterized by texture elements [12,[25][26][27][28][29][30][31][32][33][34]. As a result, the structural approaches' goals are to extract the texture elements of defects, which are used to represent the spatial placement rules.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

Cui¹,

Liu²,

Lian³

2022

Preprint

View full text Add to dashboard Cite

In line with the development of Industry 4.0, more and more attention is attracted to the field of surface defect detection. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in industry field, where deep learning-based algorithms performs better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and a significant amount of labor, but it is also inefficient and has certain limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling above disadvantages for visual anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, information on publicly available datasets containing surface image samples are provided. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective.

show abstract

Section: Related Workmentioning

confidence: 99%

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

Cui¹,

Liu²,

Lian³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…These methods are divided into the following categories: spatial statistical analysis, spectral analysis, model-based methods, and dictionary learning. Spatial statistical methods detect defects by calculating gray values contrasted with their surroundings, including histogram character analysis [8], morphology [9], local contrast enhancement [10], and the fractal method [11]. The detection results of these methods depend largely on the size of a selected window and its discrimination threshold; it is difficult to detect smaller sizes defects for them.…”

Section: Related Workmentioning

confidence: 99%

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

Gao

Liu

et al. 2019

IEEE Access

View full text Add to dashboard Cite

In order to accurately detect defects in patterned fabric images, a novel detection algorithm based on Gabor-HOG (GHOG) and low-rank decomposition is proposed in this paper. Defect-free pattern fabric images have the specified direction, while defects damage their regularity of direction. Therefore, a direction-aware descriptor is designed, denoted as GHOG, a combination of Gabor and HOG, which is extremely valuable for localizing the defect region. Upon devising a powerful directional descriptor, an efficient low-rank decomposition model is constructed to divide the matrix generated by the directional feature extracted from image blocks into a low-rank matrix (background information) and a sparse matrix (defect information). A nonconvex log det(·) as a smooth surrogate function for the rank instead of the nuclear norm is also exploited to improve the efficiency of the low-rank model. Moreover, the computational efficiency is further improved by utilizing the alternative direction method of multipliers (ADMM). Thereafter, the saliency map generated by the sparse matrix is segmented via the optimal threshold algorithm to locate the defect regions. Experimental results show that the proposed method can effectively detect patterned fabric defects and outperform the state-of-the-art methods.

show abstract

“…They were based on using difference between the dilation and erosion of the target image. 25,26 It can simplify image data by maintaining primary shape characteristics and remove irrelevant details.…”

Section: Introductionmentioning

confidence: 99%

Simulation evaluation of knitted mesh structure using morphological operations

Tian

et al. 2022

Journal of Engineered Fibers and Fabrics

View full text Add to dashboard Cite

High-quality simulation of fabric structural features is significant for realizing the morphological prediction of nonuniform mesh structures and building numerical simulation of physical properties. For lace textiles which have hundreds and thousands of variable meshes, an objective and unified standard to evaluate structural deformation is quite challenging. It needs to comprehensively and quantitatively conclude a result, instead of visual and subjective judgment. Therefore, this paper proposed an image-based method to mathematically solve accuracy by comparing morphological features from both simulation result and real fabric photograph. Jacquardtronic lace textiles were fabricated as experimental samples. Based on morphological expansion and corrosion algorithms, non-characteristic lapping details were eroded from binary images with only featured single-pixel contours of irregular meshes. Shape descriptor of each featured image was represented by a moment vector of seven Hu invariant moments. Then the morphological vectors of both simulated and real fabric images were substituted into a defined equation of similarity measurement. This image-based evaluation model effectively avoids defects of subjective visual observation and geometric measurement methods.

show abstract

Detection of chemical fabric defects on the basis of morphological processing

Cited by 18 publications

References 18 publications

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

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

Simulation evaluation of knitted mesh structure using morphological operations

Contact Info

Product

Resources

About