CP-YOLOv8s algorithm for tiny and extreme-type fabric defect detection

Abstract

To overcome the hurdles in tiny and extreme aspect ratio defect identification in fabrics, along with the issues of high leakage and false detection rates, we proposed an improved YOLOv8 fabric defect detection algorithm CA-P2-YOLOv8s (CP-YOLOv8s). It mainly includes three strategies. First, the coordinate attention (CA) mechanism was integrated behind the Spatial Pyramid Pooling Fast SPPF) module, which can embed the location information into the attention of the channel to enhance the image features. Second, a P2 detection layer was added at the detection head to form a four-scale pyramid network, increasing detection accuracy of tiny defects. Finally, the Wise-ShapeIoU loss function was designed, Wise-IoU steers the model focus to the more run-of-the-mill, decent-quality anchor frames, and Shape-IoU ensures the model keeps its eye on the ball when it comes to the frame’s intrinsic shape and scale. By working in tandem, these loss functions guide the model, reducing the effects of subpar samples, and ultimately improving the network’s performance to generalize well across the board. Experiments on the AliCloud Tianchi public fabric defects dataset validated that the proposed CP-YOLOv8s algorithm’s precision, recall, and mean average precision (mAP) are 95.0%, 83.1%, and 89.5%, respectively. That represents a solid jump of 4.8%, 6.5%, and 5.1% over the baseline YOLOv8s model, all while trimming the model’s parameters by 4.2%. On the self-constructed silk fabric dataset, the algorithm outperformed other algorithms by achieving a precision of 92.8%, recall of 86.0%, and mAP of 91.4%, respectively. In addition, the detection speed is enough to keep up with the demands of real-time industrial inspection.

Keywords

Fabric defect object detection coordinate attention P2 layer Wise-ShapeIoU YOLOv8

Get full access to this article

View all access options for this article.

References

Kumar

Computer-vision-based fabric defect detection: A survey. IEEE Trans Ind Electron 2008; 55(1): 348–363.

Zhan

Zhou

Fabric defect classification using prototypical network of few-shot learning algorithm. Comput Ind 2022; 138: 103628.

Shady

Gowayed

Abouiiana

, et al. Detection and classification of defects in knitted fabric structures. Text Res J 2006; 76(4): 295–300.

Ren

Girshick

, et al. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 2016; 39(6): 1137–1149.

Cai

Vasconcelos

Cascade R-CNN: Delving into high quality object detection. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018. IEEE, 2018, pp. 6154-6162

Liu

Anguelov

Erhan

, et al. SSD: Single shot multibox detector. In: Computer Vision – ECCV 2016 (eds. Leibe B, Matas J, Sebe N, et al.). Cham: Springer, 2016, pp. 21–37.

Redmon

Divvala

Girshick

, et al. You Only Look Once: Unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016. IEEE, 2016, pp. 779–788.

Jocher

Chaurasia

Qiu

YOLO by Ultralytics. Available at: https://github.com/ultralytics/ultralytics (2023, accessed 27 July 2025).

Zhang

Qiu

, et al. A fabric defect detection algorithm based on YOLOv8. In: 2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), Chengdu, China, 3–5 November 2023. IEEE, 2023, pp. 1040–1043.

10.

Gao

Zhao

Research on textile defect detection algorithm for deep learning. Research Square preprint 2024. DOI: 10.21203/rs.3.rs-4179693/v1.

11.

Chen

Zhou

, et al. Fabric defect detection algorithm based on improved YOLOv8. Text Res J. Epub ahead of print 25 July 2024. DOI: 10.1177/00405175241261092.

12.

Nasim

Mumtaz

Ahmad

, et al. Fabric defect detection in real world manufacturing using deep learning. Information 2024; 15(8): 476.

13.

Liu

Zhang

Fabric defect detection based on improved lightweight YOLOv8n. Appl Sci 2024; 14(17): 8000.

14.

Chen

Zhou

Xiao

, et al. LWFDD-YOLO: A lightweight defect detection algorithm based on improved YOLOv8. Text Res J. Epub ahead of print 24 October 2024. DOI: 10.1177/00405175241285596.

15.

Hou

Zhou

Feng

Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021. IEEE, 2021, pp. 13713–13722.

16.

Jocher

YOLOv5 by Ultralytics. Available at: https://github.com/ultralytics/yolov5 (2020, accessed 25 July 2025).

17.

Wang

C-Y

Bochkovskiy

Liao

H-YM.

Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696, 2020.

18.

Wang

, et al. Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Adv Neural Inf Process Syst 2020; 33: 21002–21012.

19.

Zheng

Wang

Liu

, et al. Distance-IoU loss: Faster and better learning for bounding box regression. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020, pp. 12993–13000.

20.

Woo

Park

Lee

J-Y

, et al. CBAM: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 3–19.

21.

Shen

Sun

Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 2019; 42: 2011–2023.

22.

Tong

Chen

, et al. Wise-IoU: Bounding box regression loss with dynamic focusing mechanism. arXiv preprint arXiv:230110051, 2023.

23.

Zhang

Shape-IoU: More accurate metric considering bounding box shape and scale. arXiv preprint arXiv:231217663, 2023.

24.

Tianchi, Smart Diagnosis of Cloth Flaw Dataset. Available at: https://tianchi.aliyun.com/dataset/79336 (2020, accessed 25 July 2025).

25.

Yang

Zhang

R-Y

, et al. SimAM: A simple, parameter-free attention module for convolutional neural networks. In: Marina

Tong

(eds.), Proceedings of the 38th International Conference on Machine Learning. PMLR, 2021, pp. 11863–11874.

26.

Lau

L-M

Rehman

YAU.

Large separable kernel attention: Rethinking the large kernel attention design in CNN. Expert Syst Appl 2024; 236: 121352.

27.

Selvaraju

Cogswell

Das

, et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22–29 October 2017. IEEE, 2017, pp. 618–626.