SpringerLink
Log in

Transmission line abnormal target detection algorithm based on improved YOLOX

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The detection of anomalous targets in transmission lines is an important research topic for industrial applications and power grid construction. However, due to the complexity of anomalous targets in the natural environment, existing target detection algorithms have problems such as false and missed detection. To improve the detection performance of anomalous targets, we make the following improvements based on the YOLOX algorithm. First, we assign weights to important target features, extend the perceptual domain of small targets, and enhance their nonlinear representation. Second, the residual network structure is optimized to obtain the key information of the target. Finally, a feature enhancement network with an attention mechanism is proposed to enhance the visibility of anomalous targets in the feature map. The experimental results show that the detection accuracy of the detection model in this paper reaches 82.7% and 91.1% for high-voltage tower bird nest and power line insulator targets, respectively, and the detection speed can reach 72.4 FPS. Compared with the YOLOX network, our method has better detection performance.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: Optimal speed and accuracy of object detection[J]. ar**v preprint ar**v:2004.10934

  2. Bodla N, Singh B, Chellappa R et al (2017) Soft-NMS–improving object detection with one line of code[C]//Proceedings of the IEEE Int Conf Comput Vis. p 5561-5569

  3. Cao J, Shang S, Wang M et al (2023) A Novel Defect Detection Method for Insulators of Power Transmission Line Based on YOLOv5[C]. Intelligent Networked Things: 5th China Conference, CINT 2022, Urumqi, China, August 7–8, 2022, Revised Selected Papers. Springer Nature Singapore, Singapore, pp 135–146

  4. Chen Z, **ao H, Wu G (2006) Electromagnetic sensor navigation system of robot for high-voltage transmission line inspection[J]. Transducer Microsys Technol. 9:30–39

    Google Scholar 

  5. Chen J, Fu Z, Cheng X et al (2023) An method for power lines insulator defect detection with attention feedback and double spatial pyramid[J]. Electr Power Syst Res. 218:109175

    Article  Google Scholar 

  6. Cheng G, Yuan X, Yao X et al (2022) Towards large-scale small object detection: Survey and benchmarks[J]. ar**v preprint ar**v:2207.14096

  7. Creswell A, White T, Dumoulin V et al (2018) Generative adversarial networks: An overview[J]. IEEE Signal Proc Mag. 35(1):53–65

    Article  Google Scholar 

  8. Deng C, Wang M, Liu L et al (2021) Extended feature pyramid network for small object detection[J]. IEEE Transactions on Multimedia 24:1968–1979

    Article  Google Scholar 

  9. Ge Z, Liu S, Li Z et al (2021) Ota: Optimal transport assignment for object detection[C]. Proceedings of the IEEE/CVF Conf Comput Vis Pattern Recognit. p 303-312

  10. Ge Z, Liu S, Wang F et al (2021) Yolox: Exceeding yolo series in 2021[J]. ar**v preprint ar**v:2107.08430

  11. Girshick R (2015) Fast r-cnn[C]. Proceedings of the IEEE Int Conf Comput Vis. p 1440-1448

  12. Girshick R, Donahue J, Darrell T et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation[C]. Proceedings of the IEEE Conf Comput Vis Pattern Recognit. p 580-587

  13. He K, Gkioxari G, Doll r P et al (2017) Mask r-cnn[C]. Proceedings of the IEEE Int Conf Comput Vision. p 2961-2969

  14. He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition[C]. Proceedings of the IEEE Conf Comput Vis pattern Recognit. p 770-778

  15. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks[C]. Proceedings of the IEEE Conf Comput Vis Pattern Recognit. p 7132-7141

  16. Kim K, Lee HS (2020) Probabilistic anchor assignment with iou prediction for object detection[C]. Eur Conf Comput Vis. Springer, Cham, pp 355–371

  17. Kisantal M, Wojna Z, Murawski J et al (2019) Augmentation for small object detection[J]. ar**v preprint ar**v:1902.07296

  18. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks[J]. Adv Neural Info Process Syst. vol 25

  19. LeCun Y, Bengio Y, Hinton G (2015) Deep learning[J]. nature, 521(7553): 436-444

  20. Lei X, Sui Z (2019) Intelligent fault detection of high voltage line based on the Faster R-CNN[J]. Measurement 138:379–385

    Article  Google Scholar 

  21. Lin T Y, Doll r P, Girshick R et al (2017) Feature pyramid networks for object detection[C]. Proceedings of the IEEE Conf Comput Vis Pattern Recognit. p 2117-2125

  22. Lin T Y, Goyal P, Girshick R et al (2017) Focal loss for dense object detection[C]. Proceedings of the IEEE Int Conf Comput Vis. pp 2980-2988

  23. Liu W, Anguelov D, Erhan D et al (2016) Ssd: Single shot multibox detector[C]. Euro Conf Comput Vis. Springer, Cham, p 21-37

  24. Liu S, Qi L, Qin H et al (2018) Path aggregation network for instance segmentation[C]. Proceedings of the IEEE Conf Comput Vis Pattern Recognit. p 8759-8768

  25. Long X, Deng K, Wang G et al (2020) PP-YOLO: An effective and efficient implementation of object detector[J]. ar**v preprint ar**v:2007.12099

  26. Ma N, Zhang X, Sun J (2020) Funnel activation for visual recognition[C]. European Conference on Computer Vision. Springer, Cham, p 351-368

  27. Nardelli PHJ, Rubido N, Wang C et al (2014) Models for the modern power grid[J]. Eur Phys J Spec Top. 223(12):2423–2437

    Article  Google Scholar 

  28. Peungsungwal S, Pungsiri B, Chamnongthai K, et al (2001) Autonomous robot for a power transmission line inspection[C]. ISCAS 2001. The IEEE Int Symp Circ Syst. (Cat. No. 01CH37196). 3:121-124

  29. Qiu Z, Zhu X, Liao C et al (2022) Detection of transmission line insulator defects based on an improved lightweight YOLOv4 model[J]. Applied Sciences 12(3):1207

    Article  Google Scholar 

  30. Redmon J, Divvala S, Girshick R et al (2016) You only look once: Unified, real-time object detection[C]. Proceedings of the IEEE Conf Comput Vis pattern Recog. p 779-788

  31. Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger[C]. Proceedings of the IEEE Conf Comput Vis Pattern Recognit. p 7263-7271

  32. Redmon J, Farhadi A (2018) Yolov3: An incremental improvement[J]. ar**v preprint ar**v:1804.02767

  33. Ren S, He K, Girshick R et al (2015) Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Adv neural Info Process Syst. vol 28

  34. Satheeswari D, Shanmugam L, Swaroopan NMJ et al (2022) Mask R-CNN based Object Detection in Overhead Transmission Line from UAV Images[C]. Third International Conference on Image Processing and Capsule Networks: ICIPCN. Cham: Springer International Publishing, p 639-653

  35. Simard P Y, Steinkraus D, Platt JC (2003) Best practices for convolutional neural networks applied to visual document analysis[C]. Icdar. 3(2003)

  36. Song G, Liu Y, Wang X (2020) Revisiting the sibling head in object detector[C]. Proceedings of the IEEE/CVF Conf Comput Vis Pattern Recognit. p 11563-11572

  37. Su T, Liu D (2023) Transmission line defect detection based on feature enhancement[J]. Multimedia Tools and Appl. p 1-13

  38. Szegedy C, Toshev A, Erhan D (2013) Deep neural networks for object detection[J]. Adv neural info process. syst. vol 26

  39. Tian Z, Shen C, Chen H et al (2019) Fcos: Fully convolutional one-stage object detection[C]. Proceedings of the IEEE/CVF Int Conf Comput Vis. p 9627-9636

  40. Van Dyk DA, Meng XL (2001) The art of data augmentation[J]. J Comput Graph Stat. 10(1):1–50

    Article  MathSciNet  Google Scholar 

  41. Vergouw B, Nagel H, Bondt G et al (2016) Drone technology: Types, payloads, applications, frequency spectrum issues and future developments[M]. The future of drone use. TMC Asser Press, The Hague, p 21–45

  42. Wan L, Zeiler M, Zhang S, et al. Regularization of neural networks using dropconnect[C]. Int Conf Mach learn. PMLR, 2013: 1058-1066

  43. Woo S, Park J, Lee JY et al (2018) Cbam: Convolutional block attention module[C]. Proc Eur Conf Comput Vis (ECCV). p 3-19

  44. Wu Y, Chen Y, Yuan L et al (2020) Rethinking classification and localization for object detection[C]. Proceedings of the IEEE/CVF Conf Comput Vis pattern Recognit. p 10186-10195

  45. **a H, Yang B, Li Y et al (2022) An improved CenterNet model for insulator defect detection using aerial imagery[J]. Sensors 22(8):2850

    Article  Google Scholar 

  46. Yang L, Zhang R Y, Li L et al (2021) Simam: A simple, parameter-free attention module for convolutional neural networks[C]. Int Conf Mach Learn. PMLR, p 11863-11874

  47. Zhao Q, Sheng T, Wang Y et al (2019) M2det: A single-shot object detector based on multi-level feature pyramid network[C]. Proceedings of the AAAI Conf Artif Intell. 33(01):9259-9266

  48. Zhou X, Wang D, Krähenbühl P (2019) Objects as points[J]. ar**v preprint ar**v:1904.07850

  49. Zou Z, Shi Z, Guo Y et al (2019) Object detection in 20 years: A survey[J]. ar**v preprint ar**v:1905.05055

Download references

Acknowledgements

This work was supported by the Project of Shanghai Science and Technology Committee (No. 23010501500).

Author information

Authors and Affiliations

  1. College of Computer Technology and Science, Shanghai University of Electric Power, 201306, Shanghai, China

    Zhongqin Bi, Lina **g & Chao Sun

  2. Institute of Information Science and Technology, East China University of Political Science and Law, 200042, Shanghai, China

    Mei**g Shan

  3. China Electronics Technology Group Corporation, No.34 Research Institute, 541004, Guilin Guangxi, China

    Wei Zhong

Authors
  1. Zhongqin Bi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lina **g
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chao Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mei**g Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chao Sun.

Ethics declarations

Conflicts of interest

The authors declare no conflicts of interest or competing interests.

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

Bi, Z., **g, L., Sun, C. et al. Transmission line abnormal target detection algorithm based on improved YOLOX. Multimed Tools Appl 83, 53263–53278 (2024). https://doi.org/10.1007/s11042-023-16309-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16309-6

Keywords

Search

Navigation