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Abstract

Fabric defect detection is one of the hot topics in the textile industry, primarily due to occasional machine failures and repetitive errors on production lines leading to misalignment in warp and weft. Consequently, collecting defect samples is challenging, as these defects appear as subtle, linear shapes in horizontal or vertical orientations, resulting in sparse defect features with low contrast. This poses significant challenges to the model’s feature learning capabilities and detection performance. we developed a research platform for developing an automatic fabric detection system, focusing on collecting fabric defect images and developing/deploying detection algorithms. Utilizing this platform, a large number of fabric images were collected and annotated on the denim production line in textile enterprises, with the dataset being made publicly available for research within the field. Mainstream methods such as local attention or multiscale fusion have limited effectiveness in detecting defects in denim, which exhibit sparse features, varied morphologies, low contrast, and a lack of global features. This paper designs multiple sparse filters for feature extraction. Square filters are utilized to extract minute defect features in fabric images, while asymmetric filters are employed to capture linear defect characteristics, enhancing the model’s representation capabilities. Furthermore, a compact arrangement involving multiple sparse filters is proposed to enhance feature diversity in model learning and reduce the risk of overfitting. Experiments conducted on the dataset provided in this paper and the TIANCHI dataset demonstrate that the method proposed in this paper outperforms existing mainstream algorithms. The denim fabric dataset can be obtained on GitHub: https://github.com/h-z-liang/DEMIN_DATASET.git.

Keywords

Fabric defect detection sparse filter fabric dataset convolutional neural network

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References

Zhang

Ren

, and Sun

Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016; pp. 770–778.

Girshick

Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision, 2015; pp. 1440–1448.

Cheng

RetinaNet with difference channel attention and adaptively spatial feature fusion for steel surface defect detection. IEEE Trans Instrum Meas 2020; 70: 1–11.

Tan

EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of Machine Learning Research, 2019; pp. 6105–6114.

Liu

Anguelov

Erhan

, et al. SSD: Single shot multibox detector. In Proceedings of the 14th European Conference on Computer Vision (ECCV 2016), Amsterdam, The Netherlands, 11–14 October 2016, Part I 14, pp. 21–37.

Jiang

Ergu

Liu

Cai

, and Ma

A review of YOLO algorithm developments. Proc Comput Sci 2022; 199: 1066–1073.

Everingham

Van Gool

Williams

CKI

Winn

, and Zisserman

The Pascal visual object classes (VOC) challenge. Int J Comput Vis 2010; 88: 303–338.

Lin

T-Y

Maire

Belongie

, et al. Microsoft COCO: Common objects in context. In Proceedings of the 13th European Conference on Computer Vision (ECCV 2014), Zurich, Switzerland, 6–12 September 2014, Part V 13, pp. 740–755.

Zhou

Yang

, et al. Classifying fabric defects with evolving Inception v3 by improved L2, 1-norm regularized extreme learning machine. Text Res J 2023; 93(3–4): 936–956.

10.

Zhou

Wang

, et al. Fabric wrinkle rating model based on ResNet18 and optimized random vector functional-link network. Text Res J 2023; 93(1–2): 172–193.

11.

Zhou

Deng

Wang

, et al. Classification of clothing images based on a parallel convolutional neural network and random vector functional link optimized by the grasshopper optimization algorithm. Text Res J 2022; 92(9–10): 1415–1428.

12.

Zhou

Liu

Deng

, et al. Clothing image classification with DenseNet201 network and optimized regularized random vector functional link. J Nat Fibers 2023; 20(1): 2190188.

13.

Huang

A texture-aware one-stage fabric defect detection network with adaptive feature fusion and multi-task training. J Intell Manuf 2024; 35(3): 1267–1280.

14.

Zhao

Liu

, et al. Attention based multi-scale feature fusion for efficient surface defect detection. IEEE Trans Instrum Meas 2024; ▪: ▪.

15.

Guo

Kang

, and Yang

Automatic fabric defect detection method using AC-YOLOV5. Electronics 2023; 12(13): 2950.

16.

Zeng

Liu

, and Chao

Reference-based defect detection network. IEEE Trans Image Process 2021; 30: 6637–6647.

17.

Fang

Long

Sun

Liu

Zhang

, and Fang

Tactile-based fabric defect detection using convolutional neural network with attention mechanism. IEEE Trans Instrum Meas 2022; 71: 1–9.

18.

Chetverikov

Hanbury

Finding defects in texture using regularity and local orientation. Pattern Recog 2002; 35(10): 2165–2180.

19.

Ngan

HYT

Pang

GKH.

Regularity analysis for patterned texture inspection. IEEE Trans Automat Sci Eng 2008; 6(1): 131–144.

20.

Timm

Barth

Non-parametric texture defect detection using Weibull features. Image Process Machine Vis Applicat IV 2011; 7877: 150–161.

21.

Zhang

Feng

Wang

, and Wang

ZJU-Leaper: A benchmark dataset for fabric defect detection and a comparative study. IEEE Trans Artif Intell 2020; 1(3): 219–232.

22.

TIANCHI, Smart diagnosis of cloth flaw dataset. https://tianchi.aliyun.com/dataset/dataDetail?dataId=79336 (2020)

23.

Simonyan

Very deep convolutional networks for large-scale image recognition. arxiv preprint arxiv:1409.1556, 2014.

24.

Vanhoucke

Learning visual representations at scale. ICLR Invited Talk 2014; 1: 2.

25.

Wang

Liu

, and Foroosh

Factorized convolutional neural networks. In Proceedings of the IEEE International Conference on Computer Vision Workshops, 2017; pp. 545–553.

26.

Chollet

Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017; pp. 1251–1258.

27.

Howard

AG.

MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017.

28.

Liu

Mao

C-Y

Feichtenhofer

Darrell

, and Xie

A ConvNet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022; pp. 11976–11986.

29.

Sandler

Howard

Zhu

Zhmoginov

, and Chen

L-C.

MobileNetV2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018; pp. 4510–4520.

30.

Liu

Chen

, et al. More convnets in the 2020s: Scaling up kernels beyond 51x51 using sparsity. arXiv preprint arXiv:2207.03620, 2022.

31.

Trockman

Kolter

JZ.

Patches are all you need? arXiv preprint arXiv:2201.09792, 2022.

32.

Ding

Zhang

Han

, and Ding

Scaling up your kernels to 31 × 31: Revisiting large kernel design in CNNs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022; pp. 11963–11975.

33.

Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122, 2015.

34.

Fang

Qiu

, and Xu

An anchor-free defect detector for complex background based on pixelwise adaptive multiscale feature fusion. IEEE Trans Instrum Meas 2022; 72: 1–12.

35.

Liu

Ren

Feature purification fusion structure for fabric defect detection. Vis Comput 2024; 40(5): 3825–3842.

36.

Tao

Adak

Chun

P-J

Yan

, and Liu

VitalNet: Anomaly on industrial textured surfaces with hybrid transformer. IEEE Trans Instrum Meas 2023; 72: 1–13.

37.

Xiao

, et al. Automatic fabric defect detection using a wide-and-light network. Appl Intell 2021; 51(7): 4945–4961.

38.

Deng

Liang

, and Liu

Context receptive field and adaptive feature fusion for fabric defect detection. Soft Comput 2023; 27(18): 13421–13434.

39.

Lin

Zhou

Kang

, and Wang

EMRA-Net: A pixel-wise network fusing local and global features for tiny and low-contrast surface defect detection. IEEE Trans Instrum Meas 2022; 71: 1–14.

40.

Redmon

YOLOV3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018.

41.

Liu

Qin

Shi

, and Jia

Path aggregation network for instance segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018; pp. 8759–8768.

42.

Wang

C-Y

Yeh

I-H

, and Liao

H-YM.

YOLOv9: Learning what you want to learn using programmable gradient information. arXiv preprint arXiv:2402.13616, 2024.

43.

Wen

, and He

ScConv: Spatial and channel reconstruction convolution for feature redundancy. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023; pp. 6153–6162.

44.

Tang

Zhang

Cao

, and Zhang

CGNet: A light-weight context guided network for semantic segmentation. IEEE Trans Image Process 2020; 30: 1169–1179.

45.

Zhang

Zheng

H-T

, and Sun

ShuffleNet v2: Practical guidelines for efficient CNN architecture design. In Proceedings of the European Conference on Computer Vision (ECCV), 2018; pp. 116–131.

46.

Tang

Han

Guo

, and Wang

GhostNetv2: Enhance cheap operation with long range attention. Adv Neur Inform Process Syst 2022; 35: 9969–9982.

47.

, et al. Efficientformer: Vision transformers at MobileNet speed. Adv Neur Inform Process Syst 2022; 35: 12934–12949.