Skip to main content
SpringerLink
Log in

AE-FPN: adaptive enhance feature learning for detecting wire defects

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Wire defects usually occur in high-altitude transmission lines, leading to line transmission failures and even the possibility of large-scale power outages. Therefore, timely and accurate locating wire defects detection is a key technology for power transmission. However, there are still challenges for wire defect objects with large aspect ratios, arbitrary orientations, and complex backgrounds. In this paper, we design a novel Adaptive Enhancement Feature Pyramid Network (AE-FPN) to focus on the wire defect features through an attention mechanism during feature fusion and extraction. AE-FPN is a plug-and-play component that can be used in different networks. Using AE-FPN in a basic Faster R-CNN system, our method achieves a 3.2% AP gain at a very marginal extra cost. In addition, a multi-scenario multi-object dataset of wire defects is established that provides the baseline for detecting wire defects in transmission lines.

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

Similar content being viewed by others

Data availability

Not applicable.

References

  1. Jenssen, R., Roverso, D.: Automatic autonomous vision-based power line inspection: a review of current status and the potential role of deep learning. Int. J. Electr. Power Energy Syst. 99, 107–120 (2018)

    Article  Google Scholar 

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

  3. Kirillov, A., Girshick, R., He, K., Dollár, P.: Panoptic feature pyramid networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 6399–6408 (2019)

  4. Jiang, X., Xia, Y., Hu, J., Zhang, Z., Shu, L., Sun, C.: An S-transform and support vector machine (SVM)-based online method for diagnosing broken strands in transmission lines. Energies 4(9), 1278–1300 (2011)

    Article  Google Scholar 

  5. Zhao, L., Huang, X., Jia, J., Zhu, Y., Cao, W.: Detection of broken strands of transmission line conductors using fiber Bragg grating sensors. Sensors 18(7), 2397 (2018)

    Article  Google Scholar 

  6. Zhang, Y., Huang, X., Jia, J., Liu, X.: A recognition technology of transmission lines conductor break and surface damage based on aerial image. IEEE Access 7, 59022–59036 (2019)

    Article  Google Scholar 

  7. Zhao, Q., Qin, M., Chen, C., Zhou, X.: A study on the design of image recognition-based UAV transmission line broken strand detection system. In: Proceedings of 2019 IERI International Conference on Economics, Management, Applied Sciences and Social Science (EMAS 2019), vol. 127, pp 580–584. Advances in Education Research (2019)

  8. Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn. 13(2), 111–122 (1981)

    Article  MATH  Google Scholar 

  9. Pan, Y., Liu, F., Yang, J., Zhang, W., Li, Y., Lai, C. S., et al.:Broken power strand detection with aerial images: a machine learning based approach. In: 2020 IEEE International Smart Cities Conference (ISC2), pp. 1–7. IEEE (2020, September)

  10. Du, W., Zhang, M., Shi, X., Mao, M., Chen, Y., Feng, J.: Transmission line defect detection based on AG-RetinaNet. In: 2021 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence (ICSMD), pp. 1–6 (2021). https://doi.org/10.1109/ICSMD53520.2021.9670791

  11. Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., et al.:Attention u-net: Learning where to look for the pancreas. arXiv:1804.03999 (2018)

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

  13. Mao, M., Chen, Y., Chen, W., Du, W., Zhang, M., Mao, T.: Power transmission line image segmentation method based on binocular vision and feature pyramid network. In: 2021 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence (ICSMD), pp. 1–4 (2021). https://doi.org/10.1109/ICSMD53520.2021.9670824

  14. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)

    Article  Google Scholar 

  15. Vaillant, R., Monrocq, C., Le Cun, Y.: Original approach for the localisation of objects in images. IEE Proc. Vis. Image Signal Process. 141(4), 245–250 (1994)

    Article  Google Scholar 

  16. Felzenszwalb, P.F., Girshick, R.B., McAllester, D.: Cascade object detection with deformable part models. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2241–2248. IEEE (2010, June)

  17. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    Article  Google Scholar 

  18. Moranduzzo, T., Melgani, F.: Detecting cars in UAV images with a catalog-based approach. IEEE Trans. Geosci. Remote Sens. 52(10), 6356–6367 (2014)

    Article  Google Scholar 

  19. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  20. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

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

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

  23. 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 (2015)

  24. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv:1312.6229 (2013)

  25. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., Berg, A. C.: Ssd: single shot multibox detector. In: European Conference on Computer Vision, pp. 21–37. Springer, Cham (2016, October)

  26. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

  27. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.:. Yolov4: optimal speed and accuracy of object detection. arXiv:2004.10934 (2020)

  28. Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019)

  29. Law H, D. J. C.: Detecting objects as paired keypoints. In: Lecture Notes in Computer Science, pp. 765–781 (2018)

  30. Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv:1904.07850 (2019)

  31. Fan, D.P., Wang, W., Cheng, M.M., Shen, J.: Shifting more attention to video salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8554–8564 (2019)

  32. Fu, K., Fan, D.P., Ji, G.P., Zhao, Q.: JL-DCF: Joint learning and densely-cooperative fusion framework for RGB-D salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3052–3062 (2020)

  33. Miech, A., Laptev, I., Sivic, J.: Learnable pooling with context gating for video classification. arXiv:1706.06905 (2017)

  34. Cao, C., Liu, X., Yang, Y., Yu, Y., Wang, J., Wang, Z., et al.: Look and think twice: capturing top-down visual attention with feedback convolutional neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2956–2964 (2015)

  35. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

  36. Wang, Q., Wu, B., Zhu, P., Li, P., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2020)

  37. Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)

  38. Yang, B., Bender, G., Le, Q.V., Ngiam, J.: Condconv: conditionally parameterized convolutions for efficient inference. Adv. Neural Inf. Process. Syst. 32 (2019)

  39. Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic convolution: attention over convolution kernels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11030–11039 (2020)

  40. Zhou, Y., Ren, T., Zhu, C., Sun, X., Liu, J., Ding, X., et al.: Trar: routing the attention spans in transformer for visual question answering. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2074–2084 (2021)

  41. Zhang, K., Qian, S., Zhou, J., Xie, C., Du, J., Yin, T.: ARFNet: adaptive receptive field network for detecting insulator self-explosion defects. Signal Image Video Process. 1–9 (2022)

  42. Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., et al.: MMDetection: Open mmlab detection toolbox and benchmark. arXiv:1906.07155 (2019)

  43. Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9759–9768 (2020)

  44. Zhu, K., Wu, J.: Residual attention: a simple but effective method for multi-label recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 184–193 (2021)

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

Download references

Funding

This work was supported by the Major science and technology Project of Anhui Province No. 202203a05020023, the Hefei key generic technology Research and development Project No. 2021GJ020, the Anhui Provincial Natural Science Foundation under Grant 2108085UD12.

Author information

Authors and Affiliations

  1. Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China

    Hui Zhang, Jianming Du, Chengjun Xie, Jie Zhang, Shaowei Qian & Rui Li

  2. University of Science and Technology of China, Hefei, 230026, China

    Hui Zhang & Shaowei Qian

Authors
  1. Hui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianming Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chengjun Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shaowei Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization was contributed by HZ and JD; methodology was contributed by HZ, JD and CX; experiment was contributed by HZ, JD; validation was contributed by HZ, JD and CX; investigation was contributed by JZ and SQ; resources were contributed by JZ and RL; data were contributed by HZ and JD; writing—original draft preparation, was contributed by HZ and JD; writing—review and editing, was contributed by HZ and JD and CX; CX and JZ contributed to supervision; project administration was contributed by JZ and RL; funding acquisition was contributed by JD, JZ and CX. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Jianming Du or Chengjun Xie.

Ethics declarations

Conflict of interest

Not applicable.

Ethical approval

Not applicable.

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

Zhang, H., Du, J., Xie, C. et al. AE-FPN: adaptive enhance feature learning for detecting wire defects. SIViP 17, 2145–2155 (2023). https://doi.org/10.1007/s11760-022-02429-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02429-3

Keywords

Search

Navigation