Fabric defect classification with geometric features using Bayesian classifier

Mottalib, Md. Mozaharul; Rokonuzzaman, Md.; Habib, Md. Tarek; Ahmed, Faez

doi:10.1109/icaee.2015.7506815

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…48 Zhang et al 49 segment the jacquard warp-knitted fabric image through jacquard fabric characteristics and Markov random field theory. Mottalib et al 50 used Bayesian model to accurately classify fabric defects based on geometric features of defects. Nevertheless, the model-based methods are seldom utilized due to their high dependency on data and complex calculation.…”

Section: Traditional Fabric Defect Detection Methodsmentioning

confidence: 99%

A context-aware progressive attention aggregation network for fabric defect detection

Liu

Tian

et al. 2023

Journal of Engineered Fibers and Fabrics

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Fabric defect detection plays a critical role for measuring quality control in the textile manufacturing industry. Deep learning-based saliency models can quickly spot the most interesting regions that attract human attention from the complex background, which have been successfully applied in fabric defect detection. However, most of the previous methods mainly adopted multi-level feature aggregation yet ignored the complementary relationship among different features, and thus resulted in poor representation capability for the tiny and slender defects. To remedy these issues, we propose a novel saliency-based fabric defect detection network, which can exploit the complementary information between different layers to enhance the representation features ability and discrimination of defects. Specifically, a multi-scale feature aggregation unit (MFAU) is proposed to effectively characterize the multi-scale contextual features. Besides, a feature fusion refinement module (FFR) composed of an attention fusion unit (AFU) and an auxiliary refinement unit (ARU) is designed to exploit complementary important information and further refine the input features for enhancing the discriminative ability of defect features. Finally, a multi-level deep supervision (MDS) is adopted to guide the model to generate more accurate saliency maps. Under different evaluation metrics, our proposed method outperforms most state-of-the-art methods on our developed fabric datasets.

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Section: Traditional Fabric Defect Detection Methodsmentioning

confidence: 99%

A context-aware progressive attention aggregation network for fabric defect detection

Liu

Tian

et al. 2023

Journal of Engineered Fibers and Fabrics

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“…Zhang et al 51 segmented the jacquard warp-knitted fabric image through jacquard fabric characteristics and Markov random field theory. Mottalib et al 52 used a Bayesian model accurately to classify fabric defects based on the geometric features of defects. Model-based and structural methods are less common compared with other methods, possibly due to their high dependency on data.…”

Section: Related Workmentioning

confidence: 99%

“…Mottalib et al. 52 used a Bayesian model accurately to classify fabric defects based on the geometric features of defects. Model-based and structural methods are less common compared with other methods, possibly due to their high dependency on data.…”

Section: Related Workmentioning

confidence: 99%

CACFNet: Fabric defect detection via context-aware attention cascaded feedback network

Liu

Tian

et al. 2023

Textile Research Journal

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Fabric defect detection plays an irreplaceable role in the quality control of the textile manufacturing industry, but it is still a challenging task due to the diversity and complexity of defects and environmental factors. Visual saliency models imitating the human vision system can quickly determine the defect regions from the complex texture background. However, most visual saliency-based methods still suffer from incomplete predictions owing to the variability of fabric defects and low contrast with the background. In this paper, we develop a context-aware attention cascaded feedback network for fabric defect detection to achieve more accurate predictions, in which a parallel context extractor is designed to characterize the multi-scale contextual information. Moreover, a top-down attention cascaded feedback module was devised adaptively to select the important multi-scale complementary information and then transmit it to an adjacent shallower layer to compensate for the inconsistency of information among layers for accurate location. Finally, a multi-level loss function is applied to guide our model for generating more accurate prediction results via optimizing multiple side-output predictions. Experimental results on the two fabric datasets built under six widely used evaluation metrics demonstrate that our proposed framework outperforms state-of-the-art models remarkably.

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“…Jing et al 4 have extracted the texture defect from the regular texture by the Gabor filters and classified defects using local binary patterns and Tamura method, which have a good performance for fabrics with obvious texture. Mottalib et al 5 focused on classifying fabric defects based on geometric features of defects, which leads to the problem of poor generality. Kang and Zhang 6 have proposed to incorporate the idea of the integral image into the Elo-rating algorithm(IIER), so that various fabric defects can be quickly detected.…”

Section: Introductionmentioning

confidence: 99%

Statistical classification for E-glass fiber fabric defects based on sparse coding

Jing

Ren

et al. 2019

Journal of Engineered Fibers and Fabrics

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In this research, a statistical classification algorithm based on sparse coding is presented to classify the defects on E-glass fiber fabrics adaptively. First, all images are preprocessed by being convolved with the MR8 filter banks to obtain the filter responses. For the filter response space of each type of image, we will learn a Class-specific dictionary, and all the Class-specific dictionaries are concatenated to form a complete dictionary. Then, the reconstructed contribution rate of each atom of the complete dictionary to the image filter response is counted to obtain two types of histogram features of each image. Finally, the improved sparse representation classification is used to classify test defect images based on the histogram features. The proposed adaptive classification method has achieved an average classification accuracy of 96.67% on the dataset collected onsite. The results validate the superiority of the proposed method to E-glass fiber fabrics.

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Fabric defect classification with geometric features using Bayesian classifier

Cited by 9 publications

References 17 publications

A context-aware progressive attention aggregation network for fabric defect detection

A context-aware progressive attention aggregation network for fabric defect detection

CACFNet: Fabric defect detection via context-aware attention cascaded feedback network

Statistical classification for E-glass fiber fabric defects based on sparse coding

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