Skip to main content
SpringerLink
Log in

Transmission line defect detection based on feature enhancement

  • 1230: Sentient Multimedia Systems and Visual Intelligence
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Defects in power transmission lines affect the safety operation of devices and the reliability of power supply. Automatic visual detection of defects for power transmission lines is an ongoing trend in the smart grid development. The traditional target detector has poor applicability and low accuracy for small-sized defects taken by UAVs. To address this problem, we proposed a multi-scale model based on reinforcement context (CE-SSD, Context Enhancement-SSD). Concatenating multi-scale features from different layers as context are applied in proposed method. The CEC (context enhancement component) constitute with dilated convolution and residual blocks to enhance contextual information. In addition, so as to heighten the focus on small targets and decrease the parameter size of the network, the default box was changed. Seven common transmission line defects (TLDD) including 14,400 images were used in the experiment. The results show that CE-SSD has rather higher detection accuracy than others. The accuracy ratio of CE-SSD is 66.65% on TLDD, where the accuracy ratio of small targets is 7.6% higher than that of SSD network, and 86.4% and 98.0% in bird nest detection and misplacement detection, respectively. CE-SSD also has extraordinary performance in Pascal VOC, with a detection accuracy of 80.92%.

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

Data availability

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

References

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

  2. Deng J, Dong W, Socher R, Li LJ, Li K, Li FF (2009) ImageNet: a large-scale hierarchical image database. IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops:248–255

  3. Everingham M, Zisserman A, Williams CKI et al (2005) The 2005 pascal visual object classes challenge. In machine learning challenges workshop. Springer, Berlin, Heidelberg 117–176

  4. Girshick R (2015) Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision 1440–1448

  5. Girshick R, Donahue J, Darrell T et al (2015) Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell 38(1):142–158

    Article  Google Scholar 

  6. He G, Dollar G (2020) Mask R-CNN. IEEE Trans Pattern Analysis Machine Intell 42(2):386–397

    Article  Google Scholar 

  7. Huang G, Liu Z, Van Der Maaten L et al (2017) Densely connected convolutional networks. In proc IEEE Conf Comput Vis pattern Recognit 4700–4708

  8. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning. PMLR 448–456

  9. Jiang H, Qiu X, Chen J, Liu X, Miao X, Zhuang S (2019) Insulator fault detection in aerial images based on ensemble learning with multi-level perception. IEEE Access 7:61797–61810

    Article  Google Scholar 

  10. Li J, Yan D, Luan K, Li Z, Liang H (2020) Deep learning-based Bird's Nest detection on transmission lines using UAV imagery. Appl Sci 10(18):6147

    Article  Google Scholar 

  11. Li H, Liu L, Jun D, Jiang F, Guo F, Hu Q, Lin F (2022) An improved YOLOv3 for foreign objects detection of transmission lines. IEEE Access 10:45620–45628

    Article  Google Scholar 

  12. Liang H, Zuo C, Wei W (2020) Detection and evaluation method of transmission line defects based on deep learning. IEEE Access 8:38448–38458

    Article  Google Scholar 

  13. Lim J S, Astrid M, Yoon H J, et al (2021) Small object detection using context and attention. International conference on artificial intelligence in information and communication (ICAIIC). IEEE 181-186

  14. Lin TY, Maire M, Belongie S et al (2014) Microsoft coco: common objects in context. Eur Conf Comput Vision:740–755

  15. Lin TY, Goyal P, Girshick R et al (2017) Focal loss for dense object detection. IEEE Transactions on Pattern Analysis & Machine Intelligence 99:2999–3007

    Google Scholar 

  16. Liu W, Anguelov D, Erhan D et al (2016) SSD: Single Shot MultiBox Detector. In: Ssd: single shot multibox detector. In European Conference on Computer Vision. Springer, Cham, pp 21–37

    Google Scholar 

  17. Liu Z, Wu G, He W, Fan F, Ye X (2022) Key target and defect detection of high-voltage power transmission lines with deep learning. Int J Electr Power Energy Syst 142:108277

    Article  Google Scholar 

  18. Lu Y, Chen Y, Zhao D, et al (2019) Graph-FCN for image semantic segmentation. International symposium on neural networks. Springer, Cham 97-105

  19. Miao X, Liu X, Chen J, Zhuang S, Fan J, Jiang H (2019) Insulator detection in aerial images for transmission line inspection using single shot multibox detector. IEEE Access 7:9945–9956

    Article  Google Scholar 

  20. Minjeong Ju, Chang D, Yoo (2019) Detection of Bird's Nest in real time based on relation with electric pole using deep neural network. 2019 34th international technical conference on circuits/systems. Computers and Communications (ITC-CSCC)

  21. Nie X, Yang M, Liu RW (2019) Deep neural network-based robust ship detection under different weather conditions. 2019 IEEE intelligent transportation systems conference, ITSC 2019. Inst Electr Electr Eng Inc 47–52

  22. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint. arXiv:1804.02767

  23. Redmon J, Divvala S, Girshick R et al (2016) You only look once: unified, real-time object detection. In Proc IEEE Conf Comput Vis Pattern Recognit:779–788

  24. Ren S, He K, Girshick R, Sun J (2016) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149

    Article  Google Scholar 

  25. Shah N A, Gupta D, Lodaya R et al (2021) Colorectal Cancer segmentation using Atrous convolution and residual enhanced UNet. arXiv preprint. arXiv:2103.09289

  26. Yu C, Wang J, Gao C, et al (2020) Context prior for scene segmentation. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 12416-12425

  27. Zhao L, Li S (2020) Object detection algorithm based on improved YOLOv3. Electronics 9(3):537

    Article  Google Scholar 

  28. Zou Z, Shi Z, Guo Y et al (2019) Object detection in 20 years: a survey. arXiv preprint. arXiv:1905.05055

  29. 郑晨斌, 张勇, 胡杭等 (2020) 目标检测强化上下文模型. 浙江大学学报:工学版 54(3):11

Download references

Author information

Authors and Affiliations

  1. College of Computer Technology and Science, Shanghai University of Electric Power, Shanghai, 200090, China

    Tongtong Su & Daming Liu

Authors
  1. Tongtong Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daming Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tongtong Su.

Ethics declarations

Conflicts of interests/competing interests

The authors have no financial or proprietary interests in any material discussed 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

Su, T., Liu, D. Transmission line defect detection based on feature enhancement. Multimed Tools Appl 83, 36419–36431 (2024). https://doi.org/10.1007/s11042-023-15063-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15063-z

Keywords

Search

Navigation