Identification and Classification of Fabric Defects.

Singh, Karanveer; Kaleka, Jaspreet

doi:10.21474/ijar01/1314

Cited by 5 publications

(2 citation statements)

References 6 publications

(6 reference statements)

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“…Fabric defects in this industry are mainly caused by machine failures, yarn issues, poor finishing, and excessive stretching, leading to profit losses of 45-65%. Early detection of fabric defects is crucial for minimizing losses [1]. Previously, manual visual inspection was used; however, it had drawbacks such as high labor costs, slow detection speed, and high error rates.…”

Section: Introductionmentioning

confidence: 99%

A Fabric Defect Segmentation Model Based on Improved Swin-Unet with Gabor Filter

Xu,

Liu,

Duan

et al. 2023

Applied Sciences

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Fabric inspection is critical in fabric manufacturing. Automatic detection of fabric defects in the textile industry has always been an important research field. Previously, manual visual inspection was commonly used; however, there were drawbacks such as high labor costs, slow detection speed, and high error rates. Recently, many defect detection methods based on deep learning have been proposed. However, problems need to be solved in the existing methods, such as detection accuracy and interference of complex background textures. In this paper, we propose an efficient segmentation algorithm that combines traditional operators with deep learning networks to alleviate the existing problems. Specifically, we introduce a Gabor filter into the model, which provides the unique advantage of extracting low-level texture features to solve the problem of texture interference and enable the algorithm to converge quickly in the early stages of training. Furthermore, we design a U-shaped architecture that is not completely symmetrical, making model training easier. Meanwhile, multi-stage result fusion is proposed for precise location of defects. The design of this framework significantly improves the detection accuracy and effectively breaks through the limitations of transformer-based models. Experimental results show that on a dataset with one class, a small amount of data, and complex sample background texture, our method achieved 90.03% and 33.70% in ACC and IoU, respectively, which is almost 10% higher than other previous state of the art models. Experimental results based on three different fabric datasets consistently show that the proposed model has excellent performance and great application potential in the industrial field.

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Section: Introductionmentioning

confidence: 99%

A Fabric Defect Segmentation Model Based on Improved Swin-Unet with Gabor Filter

Xu,

Liu,

Duan

et al. 2023

Applied Sciences

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“…In the process of fabric manufacturing, the defects in the previous stage will affect the later stage. erefore, early detection of fabric defects can reduce the loss of enterprises earlier and faster [8]. erefore, effective fabric defect detection is one of the key measures for modern fabric manufacturers to control cost and enhance product value and core competence.…”

Section: Introductionmentioning

confidence: 99%

Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

et al. 2021

Security and Communication Networks

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Defects in the textile manufacturing process lead to a great waste of resources and further affect the quality of textile products. Automated quality guarantee of textile fabric materials is one of the most important and demanding computer vision tasks in textile smart manufacturing. This survey presents a thorough overview of algorithms for fabric defect detection. First, this review briefly introduces the importance and inevitability of fabric defect detection towards the era of manufacturing of artificial intelligence. Second, defect detection methods are categorized into traditional algorithms and learning-based algorithms, and traditional algorithms are further categorized into statistical, structural, spectral, and model-based algorithms. The learning-based algorithms are further divided into conventional machine learning algorithms and deep learning algorithms which are very popular recently. A systematic literature review on these methods is present. Thirdly, the deployments of fabric defect detection algorithms are discussed in this study. This paper provides a reference for researchers and engineers on fabric defect detection in textile manufacturing.

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