Skip to main content
SpringerLink
Log in

A texture-aware one-stage fabric defect detection network with adaptive feature fusion and multi-task training

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Fabric defect detection is an indispensable process to guarantee product quality in industrial production. With the proposal of industry 4.0, manufacturing enterprises have been endeavoring to develop automatic fabric defect detection systems to overcome the shortcomings of traditional manual defect detection, and many researchers have been devoting themselves to designing fabric defect detection methods with high effectiveness and strong anti-noise capacity. In the paper, we notice that the perception of the normal fabric textures is beneficial to detecting fabric defects, which is ignored in previous researches for classic one-stage detection network. Based on this finding, we propose a texture-aware one-stage fabric defect detection network (TADet). First, an adaptive feature fusion module is designed to merge and enhance multi-resolution features, where the attention mechanism is employed to make the feature fusion process adaptive to the input fabric images with different textures. Second, a multi-task defect detection head is proposed for forcing the network to distinguish the defect regions and texture regions while classifying and locating defects simultaneously in the training phase. With the defect-texture distinguishing tasks, the network is able to consider the fabric textures explicitly when detecting defects. The experimental results show that the proposed TADet outperforms other two-stage models by 3.7% and one-stage detection models by 9.3% on mAP. Besides, further experiments verify the high efficiency and strong noise-robustness of the proposed TADet, which shows its potential for industrial applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495.

    Article  PubMed  Google Scholar 

  • Ben Gharsallah, M., & Ben Braiek, E. (2021). A visual attention system based anisotropic diffusion method for an effective textile defect detection. The Journal of The Textile Institute, 112(12), 1925–1939.

    Article  Google Scholar 

  • Cai, Z., & Vasconcelos, N. (2018). Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6154–6162).

  • Cao, J., Yang, G., & Yang, X. (2020). A pixel-level segmentation convolutional neural network based on deep feature fusion for surface defect detection. IEEE Transactions on Instrumentation and Measurement, 70, 1–12.

    Google Scholar 

  • Chen, R., Cai, D., Hu, X., Zhan, Z., & Wang, S. (2021). Defect detection method of aluminum profile surface using deep selfattention mechanism under hybrid noise conditions. IEEE Transactions on Instrumentation and Measurement, 70, 1–9.

    Google Scholar 

  • Chen, S.-H., & Tsai, C.-C. (2021). Smd led chips defect detection using a yolov3-dense model. Advanced Engineering Informatics, 47, 101255.

    Article  Google Scholar 

  • Cheng, X., & Yu, J. (2020). Retinanet with difference channel attention and adaptively spatial feature fusion for steel surface defect detection. IEEE Transactions on Instrumentation and Measurement, 70, 1–11.

    Google Scholar 

  • Fang, H., Xia, M., Liu, H., Chang, Y., Wang, L., & Liu, X. (2021). Automatic zipper tape defect detection using two-stage multi-scale convolutional networks. Neurocomputing, 422, 34–50.

    Article  Google Scholar 

  • He, Y., Song, K., Meng, Q., & Yan, Y. (2019). An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Transactions on Instrumentation and Measurement, 69(4), 1493–1504.

    Article  ADS  Google Scholar 

  • He, Y., Zhu, C., Wang, J., Savvides, M., & Zhang, X. (2019). Bounding box regression with uncertainty for accurate object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2888–2897).

  • Hu, B., & Wang, J. (2020). Detection of pcb surface defects with improved faster-rcnn and feature pyramid network. IEEE Access, 8, 108335–108345.

    Article  Google Scholar 

  • Hu, G., Yang, Y., Yi, D., Kittler, J., Christmas, W., Li, S.Z., & Hospedales, T. (2015). When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition. In Proceedings of the IEEE International Conference on Computer Vision Workshops (pp. 142–150).

  • Jing, J., Wang, Z., Rätsch, M., & Zhang, H. (2022). Mobile-unet: An efficient convolutional neural network for fabric defect detection. Textile Research Journal, 92(1–2), 30–42.

    Article  CAS  Google Scholar 

  • Kim, J., Ko, J., Choi, H., & Kim, H. (2021). Printed circuit board defect detection using deep learning via a skip-connected convolutional autoencoder. Sensors, 21(15), 4968.

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  • Kisantal, M., Wojna, Z., Murawski, J., Naruniec, J., & Cho, K. (2019). Augmentation for small object detection. arXiv:1902.07296

  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90.

    Article  Google Scholar 

  • Li, C., Li, J., Li, Y., He, L., Fu, X., & Chen, J. (2021). Fabric defect detection in textile manufacturing: A survey of the state of the art. Security and Communication Networks, 2021, 1–13.

    Google Scholar 

  • Lin, H., Li, B., Wang, X., Shu, Y., & Niu, S. (2019). Automated defect inspection of led chip using deep convolutional neural network. Journal of Intelligent Manufacturing, 30(6), 2525–2534.

    Article  Google Scholar 

  • Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2117– 2125).

  • Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2980–2988).

  • Liu, Z., Liu, S., Li, C., Ding, S., & Dong, Y. (2018). Fabric defects detection based on SSD. In Proceedings of the 2nd International Conference on Graphics and Signal Processing (pp. 74–78)

  • Medak, D., Posilović, L., Subašić, M., Budimir, M., & Lončarić, S. (2021). Automated defect detection from ultrasonic images using deep learning. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 68(10), 3126–3134.

    Article  PubMed  Google Scholar 

  • Peng, P., Wang, Y., Hao, C., Zhu, Z., Liu, T., & Zhou, W. (2020). Automatic fabric defect detection method using Pran-net. Applied Sciences, 10(23), 8434.

    Article  CAS  Google Scholar 

  • Rebhi, A., Benmhammed, I., Abid, S., & Fnaiech, F. (2015). Fabric defect detection using local homogeneity analysis and neural network. Journal of Photonics, 2015, 1–9.

    Article  Google Scholar 

  • Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv:1804.02767

  • Ren, R., Hung, T., & Tan, K. C. (2017). A generic deep-learning-based approach for automated surface inspection. IEEE Transactions on Cybernetics, 48(3), 929–940.

    Article  PubMed  Google Scholar 

  • Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 28, 91–99.

    Google Scholar 

  • Shi, B., Liang, J., Di, L., Chen, C., & Hou, Z. (2021). Fabric defect detection via lowrank decomposition with gradient information and structured graph algorithm. Information Sciences, 546, 608–626.

    Article  MathSciNet  Google Scholar 

  • Sun, Y., Wang, S., Chen, C., & Xiang, T.-Z. (2022). Boundary-guided camouflaged object detection. arXiv:2207.00794

  • Tiwari, V., & Sharma, G. (2015). Automatic fabric fault detection using morphological operations on bit plane. International Journal of Computer Science and Network Security, 15(10), 30.

    Google Scholar 

  • Wang, H., Li, Z., & Wang, H. (2021). Few-shot steel surface defect detection. IEEE Transactions on Instrumentation and Measurement, 71, 1–12.

    Google Scholar 

  • Xu, X., Chen, J., Zhang, H., & Ng, W. W. (2021). D4net: De-deformation defect detection network for non-rigid products with large patterns. Information Sciences, 547, 763–776.

    Article  Google Scholar 

  • Zeng, N., Wu, P., Wang, Z., Li, H., Liu, W., & Liu, X. (2022). A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection. IEEE Transactions on Instrumentation and Measurement, 71, 1–14.

    Google Scholar 

  • Zhang, C., Feng, S., Wang, X., & Wang, Y. (2020). Zju-leaper: A benchmark dataset for fabric defect detection and a comparative study. IEEE Transactions on Artificial Intelligence, 1(3), 219–232.

    Article  Google Scholar 

  • Zhang, H., Chang, H., Ma, B.,Wang, N., & Chen, X. (2020). Dynamic r-cnn: Towards high quality object detection via dynamic training. In European Conference on Computer Vision (pp. 260–275).

  • Zhu, J.-Y., Park, T., Isola, P., & Efros, A.A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2223–2232).

Download references

Acknowledgements

This work was supported by the National Key R & D Program of China under grant 2022ZD0115401 and Lenovo (Beijing) Co. Ltd. under grant 202303030073.

Author information

Authors and Affiliations

  1. Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, 100084, China

    Bingyu Lu & Biqing Huang

Authors
  1. Bingyu Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Biqing Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Biqing Huang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, B., Huang, B. A texture-aware one-stage fabric defect detection network with adaptive feature fusion and multi-task training. J Intell Manuf 35, 1267–1280 (2024). https://doi.org/10.1007/s10845-023-02105-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-023-02105-4

Keywords

Search

Navigation