Denim Defect Detection Based on Optimal Gabor Filter

Qingchen, 王清晨 Wang; Junfeng, 景军锋 Jing; Lei, 张蕾 Zhang; Xiaohua, 王晓华 Wang; Pengfei, 李鹏飞 Li

doi:10.3788/lop55.071501

Cited by 3 publications

(2 citation statements)

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“…Traditional fabric defect detection models include the statistical methods [30][31][32] and the filter-based methods. [33][34][35] The statistical methods realize the fabric defect detection by analyzing the spatial distribution feature of the pixel value. In particular, the defect-free area on the fabric surface has stable statistical features, but the occurrence of defects will destroy the stability.…”

Section: Traditional Fabric Defect Detection Modelsmentioning

confidence: 99%

FocusDet: towards high-quality digital printing fabric defect detection

Su,

Lu,

et al. 2023

Textile Research Journal

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Deep-learning models have been effectively applied to the fabric defect detection field, in which dilemmas still exist for further improving product quality. For the self-built digital printing fabric defect detection dataset, the dilemmas can be expressed in aspects. First, the existing detection models are more inclined to learn many shot categories (head classes) and directly ignore low shot categories (tail classes); Second, the sampled positive and negative anchors in each mini-batch are not equally important, therefore they should be unequally attended to according to their importance. To solve these problems, in this article, a high-quality model for digital printing fabric defect detection was proposed, termed FocusDet. Specially, we construct the model based on the Faster-RCNN framework with two well-designed modules: the balanced group softmax module and the importance-based sample reweighting module, which improve the detection accuracy. Experimental results demonstrate that our proposed model reaches state-of-the-art accuracy on COCO metrics compared with other advanced detection models in the digital printing fabric defect detection dataset.

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Section: Traditional Fabric Defect Detection Modelsmentioning

confidence: 99%

FocusDet: towards high-quality digital printing fabric defect detection

Su,

Lu,

et al. 2023

Textile Research Journal

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“…The spectral method is based on spatial frequency domain features, which are less sensitive to noise and intensity changes compared to features extracted from the spatial domain. It includes well-known techniques such as Fourier transform [19], wavelet transform [20], and Gabor transform [21], which have been extensively studied for fabric defect detection to eliminate image texture. In [10], the authors developed the Wavelet Preprocessing Gold Image Subtraction (WGIS) method for defect detection of patterned fabrics or repetitive pattern textures.…”

Section: Traditional Approachesmentioning

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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Fabric Defect Segmentation System Based on a Lightweight GAN for Industrial Internet of Things

Zou

Zhu

et al. 2022

Wireless Communications and Mobile Computing

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Machine vision systems based on deep learning play an important role in the industrial Internet of things (IIoT) and Industry 4.0 applications, especially for product quality monitoring. Fabric defect detection is an important task in the industrial production of textiles and is crucial for product quality assurance. In actual production, the detection of many small and weak target defects remains challenging. Furthermore, industrial production requires high production rates and small model sizes in practice. This study proposes a lightweight segmentation system that meets real-time industrial production requirements. Herein, first, the defect sample image was repaired based on the image repair mechanism of the generative adversarial network model. Then, the difference between the defect sample and the repaired sample was obtained and subsequent processing, such as denoising and enhancement, was done. Finally, the defect areas were segmented. Our model was specifically designed for the segmentation of weak and small defects. This was achieved through adversarial training, optimization of an objective function, and image processing. Experimental comparisons show that the intersection over union of the three different datasets is 77.84%, 77.85%, and 73.6% and that our model is superior to the conventional semantic segmentation model. Furthermore, our model has good image restoration quality with a low mean absolute error and high structural similarity index. Additionally, our model is lightweight, has good real-time performance, and is suitable for applications in the IIoT and industrial production lines, such as embedded systems.

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Denim Defect Detection Based on Optimal Gabor Filter

Cited by 3 publications

References 0 publications

FocusDet: towards high-quality digital printing fabric defect detection

FocusDet: towards high-quality digital printing fabric defect detection

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

Fabric Defect Segmentation System Based on a Lightweight GAN for Industrial Internet of Things

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