Skip to main content
SpringerLink
Log in

Real-time and accurate defect segmentation of aluminum strip surface via a lightweight network

  • Research
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

On the premise of ensuring the defect segmentation precision of aluminum strip surfaces, improving the segmentation speed to meet the real-time requirements of the production line is an important task. Therefore, a lightweight and efficient network is proposed for the defect segmentation of aluminum strip surfaces. In the network, the lightweight GhostNet with the proposed dilation attention mechanism embedded is used for multi-scale feature extraction. This mechanism focuses more on the critical space and channel features and obtains a large receptive field in the spatial dimension. The Ghost module-based lightweight fusion node is constructed and embedded into the bidirectional feature pyramid network (BiFPN) for more efficient integration of multi-scale features. In addition, a novel lightweight boundary refinement (LBR) block designed as a residual structure is suggested to improve the localization ability near the defect boundaries. The aluminum strip surface dataset with five kinds of common defects is created and adopted to train and test the networks. The evaluation results demonstrate that the mean intersection over union (mIoU) of the proposed network is 85.51%, the speed is 68.86 fps, and the model volume is 9.38 MB. In summary, the proposed network gets a good trade-off between defect segmentation speed and accuracy for aluminum strip surfaces, which provides the potential for real-time segmentation on embedded systems.

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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Wei, W., Deng, D., Zeng, L., Zhang, C.: Real-time implementation of fabric defect detection based on variational automatic encoder with structure similarity. J. Real-Time Image Process. 18, 807–823 (2020)

  2. Feng, C., Zhang, H., Li, Y., Wang, S., Wang, H.: Efficient real-time defect detection for spillway tunnel using deep learning. J. Real-Time Image Process. 18, 2377–2387 (2021)

    Article  Google Scholar 

  3. Rayhana, R., Jiao, Y., Liu, Z., Wu, A., Kong, X.: Real-time embedded system for valve detection in water pipelines. J. Real-Time Image Process. 19, 247–259 (2022)

    Article  Google Scholar 

  4. Zheng, Z., Yang, H., Zhou, L., Yu, B., Zhang, Y.: HLU2-Net: A Residual U-Structure Embedded U-Net With Hybrid Loss for Tire Defect Inspection. IEEE Trans. Instrum. Meas. 70, 1–11 (2021)

    Article  Google Scholar 

  5. Zheng, Z., Hu, Y., Yang, H., Qiao, Y., He, Y., Zhang, Y., Huang, Y.: AFFU-Net: Attention feature fusion U-Net with hybrid loss for winter jujube crack detection. Comput. Electron. Agric. 198, 107049 (2022)

    Article  Google Scholar 

  6. Pan, Y., Lu, R., Zhang, T.: Fpga-accelerated textured surface defect segmentation based on complete period fourier reconstruction. J. Real-Time Image Process. 17, 1659–1673 (2019)

    Article  Google Scholar 

  7. Dong, H., Song, K., He, Y., Xu, J., Yan, Y., Meng, Q.: Pga-net: Pyramid feature fusion and global context attention network for automated surface defect detection. IEEE Trans. Ind. Inform. 16(12), 7448–7458 (2019)

    Article  Google Scholar 

  8. You, L., Jiang, H., Hu, J., Chang, C.H., Chen, L., Cui, X., Zhao, M.: GPU-accelerated Faster Mean Shift with euclidean distance metrics. Computers, Software, and Applications Conference (COMPSAC), 211–216 (2021)

  9. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: Optimal speed and accuracy of object detection. ArXiv abs/2004.10934 (2020)

  10. Jocher, G.: Yolov5. https://github.com/ultralytics/yolov5. Accessed 1 Oct 2021

  11. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural. Inf .Process. Syst. 28, 91–99 (2015)

  12. Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: Ghostnet: More features from cheap operations. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1580–1589 (2020)

  13. Tan, M., Pang, R., Le, Q.V.: Efficientdet: Scalable and efficient object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10781–10790 (2020)

  14. Fan, D.-P., Ji, G.-P., Zhou, T., Chen, G., Fu, H., Shen, J., Shao, L.: Pranet: Parallel reverse attention network for polyp segmentation, vol. abs/2006.11392 (2020)

  15. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015)

  16. Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)

    Article  Google Scholar 

  17. Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2881–2890 (2017)

  18. Chen, L.-C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)

  19. Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: European Conference Computer Vision(ECCV), pp. 801–818 (2018)

  20. Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)

  21. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: International Conference on Computer Vision (ICCV), pp. 10012–10022 (2021)

  22. Dong, B., Wang, W., Fan, D.-P., Li, J., Fu, H., Shao, L.: Polyp-pvt: Polyp segmentation with pyramid vision transformers. arXiv preprint arXiv:2108.06932 (2021)

  23. Zhao, M., Jha, A., Liu, Q., Millis, B.A., Mahadevan-Jansen, A., Lu, L., Landman, B.A., Tyska, M.J., Huo, Y.: Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking. Med. Image Anal. 71, 102048 (2021)

    Article  Google Scholar 

  24. Zhao, M., Liu, Q., Jha, A., Deng, R., Yao, T., Mahadevan-Jansen, A., Tyska, M.J., Millis, B.A., Huo, Y.: VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning. In: Machine Learning in Medical Imaging (MLMI) (2021)

  25. Ren, R., Hung, T., Tan, K.C.: A generic deep-learning-based approach for automated surface inspection. IEEE Trans. Cybern. 48(3), 929–940 (2018)

    Article  Google Scholar 

  26. Jiang, S., Yang, J., Xie, H., Zhang, W., Wu, B., Yang, X.: A damage detection algorithm for aluminum workpiece based on improved segmentation and decision network. In: International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT), pp. 671–674 (2021). IEEE

  27. Zhang, Q., Ye, B., Luo, S., Cao, H.: Aluminum plate defect image segmentation using improved generative adversarial networks for eddy current detection. Laser Optoelectronics Progress 58(8), 0815002 (2021)

    Article  Google Scholar 

  28. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

  29. Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: Inverted residuals and linear bottlenecks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4510–4520 (2018)

  30. Howard, A.G., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., Le, Q.V., Adam, H.: Searching for mobilenetv3, pp. 1314–1324 (2019)

  31. Li, L., Li, M., Hu, H.: An algorithm for cigarette capsules defect detection based on lightweight faster rcnn. In: China Control Conference (CCC), pp. 8028–8034 (2021). IEEE

  32. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  33. Wang, B., Huang, F.: A lightweight deep network for defect detection of insert molding based on x-ray imaging. Sensors 21(16), 5612 (2021)

    Article  Google Scholar 

  34. Ma, Z., Li, Y., Huang, M., Huang, Q., Cheng, J., Tang, S.: A lightweight detector based on attention mechanism for aluminum strip surface defect detection. Comput. Ind. 136, 103585 (2022)

    Article  Google Scholar 

  35. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proc IEEE Conf Comp Vis Pattern Recognit (CVPR), pp. 7132–7141 (2018)

  36. Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  37. Peng, C., Zhang, X., Yu, G., Luo, G., Sun, J.: Large kernel matters–improve semantic segmentation by global convolutional network. In: Proc IEEE Conf Comp Vis Pattern Recognit (CVPR), pp. 4353–4361 (2017)

  38. Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8759–8768 (2018)

  39. Milletari, F., Navab, N., Ahmadi, S.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. CoRR abs/1606.04797 (2016)

  40. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: International Conference on Artificial Intelligence and Statistics (2010)

  41. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: Efficient channel attention for deep convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11531–11539 (2020)

  42. Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: European Conference Computer Vision(ECCV), pp. 3–19 (2018)

  43. Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13713–13722 (2021)

Download references

Acknowledgements

This work was supported by the Guangxi Specially Invited Experts Foundation of Guangxi Zhuang Autonomous Region, China (GuiRenzi2019(13)).

Author information

Authors and Affiliations

  1. Light Alloy Research Institute, Central South University, Changsha, 410083, China

    Zehua Lv, Yibo Li & Siying Qian

Authors
  1. Zehua Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yibo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Siying Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ZL: conceptualization, methodology, formal analysis, writing-original draft preparation. YL: conceptualization, writing-review and editing. SQ: investigation, validation.

Corresponding author

Correspondence to Yibo Li.

Ethics declarations

Conflict of interest

All authors declare that they have no conflicts of interest affecting the work reported in this article.

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

Lv, Z., Li, Y. & Qian, S. Real-time and accurate defect segmentation of aluminum strip surface via a lightweight network. J Real-Time Image Proc 20, 37 (2023). https://doi.org/10.1007/s11554-023-01295-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11554-023-01295-7

Keywords

Search

Navigation