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Fault detection of insulator based on saliency and adaptive morphology

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Abstract

We study the problem of glass insulator fault detection from image in this work. It is a challenging task as no reliable electromagnetism cues are available. Since the characteristics of insulator fault are not clear and the positions of the insulator fault are uncertain. Previous efforts have been focusing on insulator classification and the insulator location. Recently, there is mounting evidence that saliency detection are setting new records for various vision applications. On the other hand, considering the particularly structure of insulator, fault detection can be naturally enhanced by morphology problem. Therefore, we in this paper present a saliency and adaptive morphological based insulator fault detection algorithm, aiming to jointly explore the capacity of saliency and morphology. Specifically, we propose an adaptive learning scheme which learns the saliency and morphology in a unified adaptive framework. According to the experiment, we can process most of the circumstances with 92 % accuracy in 0.5 second on average, which suits the Unmanned Aerial Vehicle (UAV) patrol device well.

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References

  1. Deng C, Wang S, Huang Z et al (2014) Unmanned aerial vehicles for power line inspection: a cooperative way in platforms and communications. J Commun 9(9):687–692

    Article  Google Scholar 

  2. Fang T, Dong C, Hu XL (2013) Contour extraction and fault detection of insulator strings in aerial images. Journal of Shanghai Jiaotong University 47 (12):1818–1822

    Google Scholar 

  3. Fletcher SDA, Norman PJ, Galloway SJ et al (2011) Determination of protection system requirements for DC unmanned aerial vehicle electrical power networks for enhanced capability and survivability. IET Electrical Systems in Transportation 1 (4):137–147

    Article  Google Scholar 

  4. Frintrop S, Rome E, Christensen HI (2010) Computational visual attention systems and their cognitive foundations: a survey. Acm Transactions on Applied Perception 7(1):1–46

    Article  Google Scholar 

  5. Han S, Hao R, Lee J (2009) Inspection of insulators on high-voltage power transmission lines. IEEE transactions on power delivery 24(4):2319–2327

    Article  Google Scholar 

  6. Haralick RM, Sternberg SR, Zhuang X (1987) Image analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence 9 (4):532–550

    Article  Google Scholar 

  7. He S, Yang D, Li W, et al. (2015) Detection and fault diagnosis of power transmission line in infrared image. In: IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, Shenyang China, pp 431–435

  8. Lee H, Kim CH, Park S et al (2013) Vision-based automatic real time inspection of power transmission line. In: 2013 44th International Symposium on Robotics (ISR), pp 1–3

  9. Liao S, An J (2015) A robust insulator detection algorithm based on local features and spatial orders for aerial images. IEEE Geosci Remote Sens Lett 12(5):963–967

    Article  Google Scholar 

  10. Lin J, Han J, Chen F et al (2011) Defects detection of glass insulator based on color image. Power System Technology 35(1):127–133

    Google Scholar 

  11. Lin Y, Guo F, Cao L et al (2016) Person re-identification based on multi-instance multi-label learning. Neurocomputing

  12. Luque-Vega LF, Castillo-Toledo B, Loukianov A et al (2014) Power line inspection via an unmanned aerial system based on the quadrotor helicopter. In: 17th IEEE Mediterranean Electrotechnical Conference. IEEE, pp 393–397

  13. Oberweger M, Wendel A, Bischof H (2014) Visual recognition and fault detection for power line insulators. In: 19th Computer Vision Winter Workshop,K?tiny Czech Republic: p 1C8

  14. Pagnano A, Hopf M, Teti R (2013) A roadmap for automated power line inspection: Maintenance and repair. Procedia CIRP 12:234–239

    Article  Google Scholar 

  15. Rahmani A, Haddadnia J, Sanai A (2010) Intelligent detection of electrical equipment faults in the overhead substations based machine vision. In: 2010 2nd International Conference on Mechanical and Electronics Engineering (ICMEE), vol 2. IEEE, pp 141–144

  16. Reddy MJB, Chandra BK, Mohanta DK (2011) A DOST based approach for the condition monitoring of 11 kV distribution line insulators. IEEE Trans Dielectr Electr Insul 18(2):588– 595

    Article  Google Scholar 

  17. Sun F, Zhao M, Liu W et al (2014) Spacer matching and localization algorithm for transmission line video images based on directional field. Proceedings of the Csee 34(1):206–213

    Google Scholar 

  18. Tong W, Yuan J, Li BS (2010) Application of image processing in patrol inspection of overhead transmission line by helicopter. Power System Technology 34 (12):204–208

    Google Scholar 

  19. Wang YL, Yan B (2014) Vision based detection and location for cracked insulator. Computer Engineering and Design 35(2):583–587

    Google Scholar 

  20. Wolfe JM (1998) Visual search. CRC Press, Boca Raton, pp 13–73

    Google Scholar 

  21. Wu Q, An J, Lin B (2012) A texture segmentation algorithm based on PCA and global minimization active contour model for aerial insulator images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5(5):1509–1518

    Article  Google Scholar 

  22. Yao CY, Jin LJ, Yan SJ (2012) Recognition of insulator string in power grid patrol images. Journal of System Simulation 24(09):1818–1822

    Google Scholar 

  23. Zhang X, An J, Chen F (2010) A method of insulator fault detection from airborne images. In: 2010 2nd WRI Global Congress on Intelligent Systems, vol 2. IEEE, pp 200–203

  24. Zhang JJ, Han J, Zhao YB et al (2014) Insulator recognition and defects detection based on shape perceptual. Journal of Image and Graphics 19(8):1194–1201

    Google Scholar 

  25. Zhao ZB, Xu GZ, Qi YC, et al. (2016) An intelligent on-line inspection and warning system based on infrared image for transformer bushings. Recent Advances in Electrical and Electronic Engineering 9(1):53–62

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Fundamental Research Funds of China for the Central Universities under Grant(No.2014MS140), the Natural Science Foundation of Fujian Province of China(No.2014J01249), the Xiamen City Science and Technology Project(No.3502Z20153003).

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Authors and Affiliations

  1. Automation Department, North China Electric Power University, Baoding, 071003, China

    Yongjie Zhai, Di Wang, Muliu Zhang & Jiarong Wang

  2. Department of Cognitive Science, School of Information Science and Engineering, Xiamen University, Xiamen, 361005, China

    Feng Guo

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  1. Yongjie Zhai
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  2. Di Wang
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  3. Muliu Zhang
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  4. Jiarong Wang
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  5. Feng Guo
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Correspondence to Feng Guo.

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Zhai, Y., Wang, D., Zhang, M. et al. Fault detection of insulator based on saliency and adaptive morphology. Multimed Tools Appl 76, 12051–12064 (2017). https://doi.org/10.1007/s11042-016-3981-2

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  • DOI: https://doi.org/10.1007/s11042-016-3981-2

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