Skip to main content
SpringerLink
Log in

A self-adjusting transformer network for detecting transmission line defects

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

To address the limitations of traditional defect detection methods for power transmission lines, this paper proposes an intelligent defect recognition method based on self-adjusting Transformer. Firstly, a deterministic networking with a large receptive field is used to extract features from the defect images obtained during power transmission line inspections. Subsequently, a DQN is employed to select important regions containing foreground information. Secondly, a bilinear attention mechanism is utilized to project the background region feature vectors, compressing their contribution in the fused feature vectors of the foreground and background regions. Furthermore, the fused feature vectors are input into a Transformer network based on adaptive encoding layers, enabling better focus on the target region. Position-scale constraints are added to the decoding layers of the Transformer to enhance the attention’s emphasis on position-scale information, thereby accelerating the convergence speed of the Transformer. Finally, gate units are introduced in each decoding layer to adaptively adjust the structure of the Transformer decoding layers to accommodate the feature extraction requirements of different inputs. Experimental studies on aerial images of power transmission line defects were conducted, and the proposed method achieved an average detection accuracy of 89.9\(\%\). Compared with other commonly used algorithms, it demonstrated superior detection accuracy and generalization ability.

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availibility

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Luo P, Wang B, Ma H, Ma F, Wang H, Zhu D (2021) Defect recognition method with low false negative rate based on combined target detection framework. High Volt Eng 47(02):454–464

    Google Scholar 

  2. Antwi-Bekoe E, Liu G, Ainam JP, Sun G, Xie X (2022) A deep learning approach for insulator instance segmentation and defect detection. Neur Comput Appl 34(9):7253–7269

    Article  Google Scholar 

  3. Zhu S, Gao Q, Lu Y, Sun D (2018) Identification and location of insulator string based on frequency-tuned. Trans China Electrotech Soc 33(23):5573–5580

    Google Scholar 

  4. Mishra DP, Ray P (2018) Fault detection, location and classification of a transmission line. Neur Comput Appl 30:1377–1424

    Article  Google Scholar 

  5. Li B et al (2022) Multi-target detection in substation scence based on attention mechanism and feature balance. Power Syst Tech 46(06):2122–2132

    MathSciNet  Google Scholar 

  6. Mohd Amiruddin AAA, Zabiri H, Taqvi SAA, Tufa LD (2020) Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems. Neur Comput Appl 32:447–472

    Article  Google Scholar 

  7. Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929

  8. Carion N et al (2020) End-to-end object detection with Transformers. In: Proceedings of the European Conference on Computer Vision, pp 213-229

  9. Mathe S, Pirinen A, Sminchisescu C (2016) Reinforcement learning for visual object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2894-2902

  10. Pirinen A, Sminchisescu C (2018) Deep reinforcement learning of region proposal networks for object detection. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp 6945-6954

  11. Alkali AH, Saatchi R, Elphick H, Burke D (2017) Thermal image processing for real-time non-contact respiration rate monitoring. IET Circ Dev Syst 11(2):142–148

    Article  Google Scholar 

  12. Abeed MA, Biswas AK, Al-Rashid MM, Atulasimha J, Bandyopadhyay S (2017) Image processing with dipole-coupled nanomagnets: noise suppression and edge enhancement detection. IEEE Trans Elec Dev 64(5):2417–2424

    Article  ADS  CAS  Google Scholar 

  13. Li W, Zhang Q, Wang D, Sun W, Li Q (2022) Stochastic configuration networks for self-blast state recognition of glass insulators with adaptive depth and multi-scale representation. Infor Sci 604:61–79

    Article  Google Scholar 

  14. Kumar A, Kumar R (2018) Adaptive artificial intelligence for automatic identification of defect in the angular contact bearing. Neur Comput Appl 29(8):277–287

    Article  Google Scholar 

  15. Peng S, Ding L, Li W, Sun W, Li Q (2022) Research on intelligent recognition method for self-blast state of glass insulator based on mixed data augmentation. High Volt 8:668–681

    Article  Google Scholar 

  16. Zhang Q, Li W, Li H, Wang J (2020) Self-blast state detection of glass insulators based on stochastic configuration networks and a feedback transfer learning mechanism. Infor Sci 522:259–274

    Article  Google Scholar 

  17. Li W, Tao H, Li H, Chen K, Wang J (2019) Greengage grading using stochastic configuration networks and a semi-supervised feedback mechanism. Infor Sci 488:1–12

    Article  Google Scholar 

  18. Li W, Deng Y, Ding M, Wang D, Sun W, Li Q (2022) Industrial data classification using stochastic configuration networks with self-attention learning features. Neur Comput Appl 34:22047–22069

    Article  Google Scholar 

  19. Tan S, Shen Z (2017) Hybrid problem-based learning in digital image processing: a case study. IEEE Trans Educ 61(2):127–135

    Article  Google Scholar 

  20. Yang L, Fan J, Song S, Liu Y (2022) A light defect detection algorithm of power insulators from aerial images for power inspection. Neur Comput Appl 34(20):17951–17961

    Article  Google Scholar 

  21. Zhang Y, Huang X, Jia JY et al (2019) A recognition technology of transmission lines conductor break and surface damage based on aerial image. IEEE Access 7(01):59022–59036

    Article  Google Scholar 

  22. Chen JC, Yu YC, Chen Z, Han W (2021) An improved method for defect identification of transmission lines based on YOLOv3. Southern Power Syst Technol 15(1):114–120

    Google Scholar 

  23. Li R, Zhang Y, Zhai D, Xu D (2021) Pin defect detection of transmission line based on improved SSD. High Volt Eng 47(11):3795–3802

    Google Scholar 

  24. Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020) An image is worth 16x16 words: Transformers for image recognition at scale. International Conference on Learning Representations

  25. Touvron H, Cord M, Douze M, et al (2021) Training data efficient image Transformers & distillation through attention. International Conference on Machine Learning, pp 10347-10357

  26. Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al (2021) Swin Transformer: Hierarchical vision Transformer using shifted windows. Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 10012-10022

  27. Wang W, Xie E, Li X, et al (2021) Pyramid Vision Transformer: A versatile backbone for dense prediction without convolutions. Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 568-578

  28. Wang W, Xie E, Li X et al (2022) Improved baselines with pyramid vision transformer. Comput Vis Media 8(03):415–424

    Article  CAS  Google Scholar 

  29. Heo B, Yun S, Han D, Chun S, et al (2021) Rethinking spatial dimensions of vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 11936-11945

  30. Wu H, Xiao B, Codella N, et al (2021) Introducing convolutions to vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 22-31

  31. Mathe S, Pirinen A, Sminchisescu C (2016) Reinforcement Learning for Visual Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2894-2902

  32. Pirinen A, Sminchisescu C (2018) Deep Reinforcement Learning of Region Proposal Networks for Object Detection. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

  33. Uzkent B, Yeh C, Ermon S (2020) Efficient Object Detection in Large Images Using Deep Reinforcement Learning. IEEE Winter Conference on Applications of Computer Vision

  34. Al-Geelani NA, Piah MAM, Shaddad RQ (2012) Characterization of acoustic signals due to surface discharges on H.V. glass insulators using wavelet radial basis function neural networks. Appl Soft Comput 12(4):1239–1246

    Article  Google Scholar 

  35. Lan Q, Pan Y, Fyshe A, White M (2020) Maxmin q-learning: controlling the estimation bias of q-learning. arXiv preprint arXiv:2002.06487

  36. Ge S, Gao Z, Zhang B, Li P (2019) Kernelized bilinear CNN models for fine-grained visual recognition. Acta Elec Sin 47(10):2134–2141

    Google Scholar 

  37. Zhao M, Zhong S, Fu X, Tang B, Pecht M (2019) Deep residual shrinkage networks for fault diagnosis. IEEE Trans Ind Inform 16(7):4681–4690

    Article  Google Scholar 

  38. Li Z, Peng C, Yu G, Zhang X, Deng Y, Sun J (2018) Detnet: design backbone for object detection. In: Proceedings of the European conference on computer vision (ECCV), pp 334-350

  39. Yan D, Chen S, Peng G, Tan Y, Zhang Y, Wu K (2020) Live working manipulator control technology based on hierarchical deep reinforcement learning. High Volt Eng 0(2):459-471

  40. Lin TY, RoyChowdhury A, Maji S (2015) Bilinear CNN models for fine-grained visual recognition. In Proceedings of the IEEE international conference on computer vision, pp 1449-1457

  41. Sun Z, Cao S, Yang Y, Kitani KM (2021) Rethinking transformer-based set prediction for object detection. In Proceedings of the IEEE/CVF international conference on computer vision, pp 3611-3620

  42. Li S, Roshan S (2019) The associations between working memory and the effects of four different types of written corrective feedback. J Second Lang Writ 45:1–15

    Article  Google Scholar 

  43. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neur Comput 9(8):1735–1780

    Article  CAS  Google Scholar 

  44. Williams RJ (1992) Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach Learn 8(3):229–256

    Article  Google Scholar 

  45. Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–49

    Article  PubMed  Google Scholar 

  46. Jocher G, Stoken A, Borovec J, Chaurasia A, Changyu L (2020) ultralytics/yolov5. GithubRepository, YOLOv5

  47. Zhou X, Koltun V, Krähenbühl P (2020) Tracking objects as points. In Computer Vision-ECCV 2020: 16th European Conference, pp 474-490

  48. Yang L, Fan J, Song S, Liu Y (2022) A light defect detection algorithm of power insulators from aerial images for power inspection. Neur Comput Appl 34(20):17951–17961

    Article  Google Scholar 

  49. Deng F, Xie Z, Mao W, Li B, Shan Y, Wei B, Zeng H (2022) Research on edge intelligent recognition method oriented to transmission line insulator fault detection. Int J Electr Power Energy Syst 139:108054

    Article  Google Scholar 

  50. Souza BJ, Stefenon SF, Singh G, Freire RZ (2023) Hybrid-YOLO for classification of insulators defects in transmission lines based on UAV. Int J Electr Power Energy Syst 148:108982

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported in part by grants from the National Natural Science Foundation of China (62173120, 52077049, 51877060), Anhui Provincial Natural Science Foundation (2008085UD04, 2108085UD07, 2108085UD11), and 111 Project (BP0719039).

Author information

Authors and Affiliations

  1. Department of Electric Engineering and Automation, Hefei University of Technology, Hefei, 230009, China

    Weitao Li, Qianqian Tong, Jiaqin Gu, Junchen Li, Wei Sun & Qiyue Li

Authors
  1. Weitao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qianqian Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiaqin Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junchen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qiyue Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiaqin Gu.

Ethics declarations

Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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

Li, W., Tong, Q., Gu, J. et al. A self-adjusting transformer network for detecting transmission line defects. Neural Comput & Applic 36, 4467–4484 (2024). https://doi.org/10.1007/s00521-023-09319-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-09319-w

Keywords

Search

Navigation