Robust powerline equipment inspection system based on a convolutional neural network

Sensors (Switzerland)

Volumn 18, Issue 11, 2018, Pages

  Siddiqui, Zahid Ali ^a,b   Park, Unsang ^a   Lee, Sang Woong ^c   Jung, Nam Joon ^d   Choi, Minhee ^d   Lim, Chanuk ^d   Seo, Jang Hun ^e  

^a Sogang University   (South Korea)

^b NED UNIVERSITY OF ENGINEERING AND TECHNOLOGY   (Pakistan)

^c Gachon University   (South Korea)

^d Korea Electric Power Research Institute (KEPRI)   (South Korea)

^e ST Vision   (South Korea)

Author keywords

Computer vision; Convolutional neural networks; Cut out switches; Deep learning; Defect analysis; Electrical safety; Ellipse detection; Gunshot damage; Insulators; Powerline equipment

Indexed keywords

CAMERAS; COMPUTER VISION; CONVOLUTION; DAMAGE DETECTION; DEEP LEARNING; DEFECTS; ELECTRIC INSULATORS; ELECTRIC LINES; ELECTRIC POWER SYSTEMS; ELECTRIC SWITCHES; ELECTRIC WIRING; LIGHTNING PROTECTION; NEURAL NETWORKS;

CONVOLUTIONAL NEURAL NETWORK; CUT-OUT; DEFECT ANALYSIS; ELECTRICAL SAFETY; ELLIPSE DETECTION; GUNSHOT DAMAGE; POWER LINES;

ELECTRIC POWER SUPPLIES TO APPARATUS;

EID: 85056325984     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s18113837     Document Type: Article

References (48)

1. Mei, X.; Lu, T.; Wu, X.; Zhang, B. Insulator surface dirt image detection technology based on improved watershed algorithm. In Proceedings of the 2012 Asia-Pacific Power and Energy Engineering Conference, Shanghai, China, 27–29 March 2012; pp. 1–5, doi:10.1109/appeec.2012.6307691.
2. Li, J.; Li, M.; Wang, Q. A novel insulator detection method for aerial images. In Proceedings of the 9th International Conference on Computer and Automation Engineering, Sydney, Australia, 18–21 February 2017; pp. 141–144, doi:10.1145/3057039.3057065.
3. Wu, Q.; An, J. An active contour model based on texture distribution for extracting inhomogeneous insulators from aerial images. IEEE Trans. Geosci. Remote Sens. 2014, 52, 3613–3626, doi:10.1109/tgrs.2013.2274101.
4. Jiang, X.; Yuan, J.; Shu, L.; Zhang, Z.; Hu, J.; Mao, F. Comparison of DC pollution flashover performances of various types of porcelain, glass, and composite insulators. IEEE Trans. Power Deliv. 2008, 23, 1183–1190, doi:10.1109/tpwrd.2007.908779.
5. Oliveira, S.C.; Fontana, E. Optical detection of partial discharges on insulator strings of high-voltage transmission lines. IEEE Trans. Instrum. Meas. 2009, 58, 2328–2334, doi:10.1109/tim.2009.2013924.
6. Shong, K.M.; Kim, Y.S.; Kim, S.G. Images detection and diagnosis of corona discharge on porcelain insulators at 22.9kV D/L. In Proceedings of the IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, Cracow, Poland, 6–8 September 2007; pp. 462–466, doi:10.1109/demped.2007.4393138.
7. Yang, T.W. Yin, H.; Ruan, Q.Q.; da Han, J.; Qi, J.T.; Yong, Q.; Wang, Z.T.; Sun, Z.Q. Overhead power line detection from UAV video images. In Proceedings of the 19th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Auckland, New Zealand, 28–30 November 2012; pp. 74–79.
8. Yetgin, Ö.E.; Şentürk, Z.; Gerek, Ö.N. A comparison of line detection methods for power line avoidance in aircrafts. In Proceedings of the 9th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, 26–28 November 2015; pp. 241–245, doi:10.1109/eleco.2015.7394489.
9. Liu, Y.; Mejias, L.; Li, Z. Fast power line detection and localization using steerable filter for active UAV guidance. In Proceedings of the 12th International Society for Photogrammetry and Remote Sensing (ISPRS2012), Melbourne, Australia, 25 August–1 September 2012; pp. 491–496, doi:10.5194/isprsarchives-xxxix-b3-491-2012.
10. Cerón, A.; Mondragón, B.I.F.; Prieto, F. Towards visual based navigation with power line detection. In Advances in Visual Computing, ISVC 2014, Lecture Notes in Computer Science; Bebis, G., Boyle, R., Parvin, B., Koracin, D., McMahan, R., Jerald, J., Zhang, H., Drucker, S.M., Kambhamettu, C., Choubassi, M.E., et al., Eds.; Springer: Berlin, Germany, 2014; Volume 8887; pp. 827–836, ISBN 978-3-319-14249-4.
11. Sampedro, C.; Martinez, C.; Chauhan, A.; Campoy, P. A supervised approach to electric tower detection and classification for power line inspection. In Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China, 6–11 July 2014; pp. 1970–1977, doi:10.1109/ijcnn.2014.6889836.
12. Han, B.; Wang, X. Learning for tower detection of power line inspection. DEStech Trans. Comput. Sci. Eng. 2016, 216–220, doi:10.12783/dtcse/iccae2016/7194.
13. Martinez, C.; Sampedro, C.; Chauhan, A.; Campoy, P. Towards autonomous detection and tracking of electric towers for aerial power line inspection. In Proceedings of the International Conference on Unmanned Aircraft Systems (ICUAS), Orlando, FL, USA, 27–30 May 2014; pp. 284–295, doi:10.1109/icuas.2014.6842267.
14. Lin, J.; Han, J.; Chen, F. Defects detection of glass insulator based on color image. Power Syst. Technol. 2011, 35, 127–133.
15. Huang, X.; Zhang, Z. A method to extract insulator image from aerial image of helicopter patrol. Power Syst. Technol. 2010, 34, 194–197.
16. Li, W.G.; Ye, G.; Huang, F.; Wang, S.; Chang, W.Z. Recognition of insulator based on developed MPEG-7 texture feature. In Proceedings of the 3rd International Congress on Image and Signal Processing, Yantai, China, 16–18 October 2010; pp. 265–268, doi:10.1109/cisp.2010.5648283.
17. Zhang, X.; An, J.; Chen, F. A simple method of tempered glass insulator recognition from airborne image. In Proceedings of the 2010 International Conference on Optoelectronics and Image Processing, Haikou, China, 11–12 November 2010; pp. 127–130, doi:10.1109/icoip.2010.23.
18. Prasad, D.S.; Reddy, B.S. Digital image processing techniques for estimating power released from the corona discharges. IEEE Trans. Dielectr. Electr. Insul. 2017, 24, 75–82, doi:10.1109/tdei.2016.005896.
19. Dudczyk, J.; Kawalec, A. Adaptive forming of the beam pattern of microstrip antenna with the use of an artificial neural network. Int. J. Antennas Propag. 2012, 2012, 1–13, doi:10.1155/2012/935073.
20. Matuszewski, J.; Sikorska-Łukasiewicz, K. Neural network application for emitter identification. In Proceedings of the 2017 18th International Radar Symposium (IRS), Prague, Czech Republic, 28–30 June 2017; pp. 1–8, doi:10.23919/IRS.2017.8008202.
21. Zhang, J.; Yang, R. Insulators recognition for 220kv/330kv high-voltage live-line cleaning robot. In Proceedings of the 18th International Conference on Pattern Recognition (ICPR’06), Hong Kong, China, 20– 24 August 2006; pp. 630–633, doi:10.1109/icpr.2006.715.
22. Zhao, J.; Liu, X.; Sun, J.; Lei, L. Detecting insulators in the image of overhead transmission lines. In Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science; Huang, D.S., Jiang, C., Bevilacqua, V., Figueroa, J.C., Eds.; Springer: Berlin/Heidelberg, Germany, 2012; Volume 7389; ISBN 978-3-642-31588-6.
23. Liao, S.; An, J. A robust insulator detection algorithm based on local features and spatial orders for aerial images. IEEE Geosci. Remote Sens. Lett. 2015, 12, 963–967, doi:10.1109/lgrs.2014.2369525.
24. Zhai, Y.; Wu, Y.; Chen, H.; Zhao, X. A method of insulator detection from aerial images. Sens. Transducers 2014, 177, 7.
25. Li, B.; Wu, D.; Cong, Y.; Xia, Y.; Tang, Y. A method of insulator detection from video sequence. In Proceedings of the 2012 Fourth International Symposium on Information Science and Engineering, Shanghai, China, 14–16 December 2012; pp. 386–389, doi:10.1109/isise.2012.93.
26. Wang, X.; Zhang, Y. Insulator identification from aerial images using support vector machine with background suppression. In Proceedings of the 2016 International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, VA, USA, 7–10 June 2016; pp. 892–897, doi:10.1109/icuas.2016.7502544.
27. Murthy, V.S.; Tarakanath, K.; Mohanta, D.K.; Gupta, S. Insulator condition analysis for overhead distribution lines using combined wavelet and support vector machine (SVM). IEEE Trans. Dielectr. Electr. Insul. 2010, 17, 89–99, doi:10.1109/tdei.2010.5412006.
28. Murthy, V.S.; Gupta, S.; Mohanta, D.K. Digital image processing approach using combined wavelet hidden Markov model for well-being analysis of insulators. IET Image Process. 2011, 5, 171–183, doi:10.1049/iet-ipr.2009.0293.
29. Jabid, T.; Uddin, M.Z. Rotation invariant power line insulator detection using local directional pattern and support vector machine. In Proceedings of the 2016 International Conference on Innovations in Science, Engineering and Technology (ICISET), Dhaka, Bangladesh, 28–29 October 2016; pp. 1–4, doi:10.1109/iciset.2016.7856522.
30. Oberweger, M.; Wendel, A.; Bischof, H. Visual recognition and fault detection for power line insulators. In Proceedings of the 19th Computer Vision Winter Workshop, Krtiny, Czech Republic, 1 January 2014; pp. 1–8.
31. Razavian, A.S.; Azizpour, H.; Sullivan, J.; Carlsson, S. CNN features off-the-shelf: An astounding baseline for recognition. In Proceedings of the Computer Vision and Pattern Recognition Workshops, Columbus, OI, USA, 23 March 2014, doi:10.1109/cvprw.2014.131.
32. Zhao, Z.; Xu, G.; Qi, Y.; Liu, N.; Zhang, T. Multi-patch deep features for power line insulator status classification from aerial images. In Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 24–29 July 2016; pp. 3187–3194, doi:10.1109/IJCNN.2016.7727606.
33. Zhai, Y.; Chen, R.; Yang, Q.; Li, X.; Zhao, Z. Insulator fault detection based on spatial morphological features of aerial images. IEEE Access 2018, 6, 35316–35326, doi:10.1109/ACCESS.2018.2846293.
34. Zhai, Y.; Cheng, H.; Chen, R.; Yang, Q.; Li, X. Multi-saliency aggregation-based approach for insulator flashover fault detection using aerial images. Energies 2018, 11, 340, doi:10.3390/en11020340.
35. Huang, X.; Zhang, H.; Zhang, Y. Automatic identification and location technology of glass insulator self-shattering. J. Electron. Imaging 2017, 26, 1–12, doi:10.1117/1.JEI.26.6.063014.
36. Zhao, Z.; Liu, N.; Wang, L. Localization of multiple insulators by orientation angle detection and binary shape prior knowledge. IEEE Trans. Dielectr. Electr. Insul. 2015, 22, 3421–3428, doi:10.1109/TDEI.2015.004741.
37. Han, Y.; Liu, Z.; Lee, D.; Liu, W.; Chen, J.; Han, Z. Computer vision–based automatic rod-insulator defect detection in high-speed railway catenary system. Intern. J. Adv. Robot. Syst. 2018, 2018, 1–15, doi:10.1177/1729881418773943.
38. Iruansi, U.; Tapamo, J.R.; Davidson, I.E. An active contour approach to insulator segmentation. In Proceedings of the AFRICON 2015, Addis Ababa, Ethiopia, 14–17 September 2015; pp. 1–5, doi:10.1109/AFRCON.2015.7331976.
39. Redmon, J. Darknet: Open Source Neural Networks in C. Available online: http://pjreddie.com/darknet/(accessed on 2 October 2018).
40. Redmon, J.; Farhadi, A. YOLO9000: Better, faster, stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017, doi:10.1109/cvpr.2017.690.
41. Fornaciari, M.; Prati, A.; Cucchiara, R. A fast and effective ellipse detector for embedded vision applications. Patten Recognit. 2014, 47, 3693–3708, doi:10.1016/j.patcog.2014.05.012.
42. Kwon, B.K.; Teng, Z.; Roh, T.J.; Kang, D.J. Fast ellipse detection based on three point algorithm with edge angle information. Int. J. Control Autom. Syst. 2016, 14, 804–813, doi:10.1007/s12555-014-0561-y.
43. Fitzgibbon, A.; Pilu, M.; Fisher, R.B.; Direct least square fitting of ellipses. IEEE Trans. Patten Anal. Mach. Intell. 1999, 21, 476–480, doi:10.1109/34.765658.
44. Burnham, J.T.; Waidelich, R.J. Gunshot damage to ceramic and non-ceramic insulators. IEEE Trans. Power Deliv. 1997, 2, 1651–1656, doi:10.1109/61.634186.
45. Electric Power Research Institute. Field Guide: Visual Inspection of Polymer Insulators. EPRI 2014. Available online: https://www.epri.com/#/pages/product/3002003720/(accessed on 8 November 2018).
46. Roussillon, T.; Sivignon, I.; Tougne, L. Measure of circularity for parts of digital boundaries and its fast computation. Patten Recognit. 2010, 43, 37–46, doi:10.1016/j.patcog.2009.06.014.
47. Everingham, M.; Gool, L.J.V.; Williams, C.K.I.; Winn, J.M.; Zisserman, A. The pascal visual object classes (VOC) challenge. Intern. J. Comput. Vis. 2010, 88, 303–338, doi:10.1007/s11263-009-0275-4.
48. Pietrow, D.; Matuszewski, J. Objects detection and recognition system using artificial neural networks and drones. In Proceedings of the Signal Processing Symposium (SPSympo), Jachranka, Poland, 12–14 September 2017; pp. 1–5, doi:10.1109/SPS.2017.8053689.