Обзор подходов к детектированию дефектов элементов ЛЭП на изображениях в инфракрасном, ультрафиолетовом и видимом спектрах
Работая с нашим сайтом, вы даете свое согласие на использование файлов cookie. Это необходимо для нормального функционирования сайта, показа целевой рекламы и анализа трафика. Статистика использования сайта отправляется в «Яндекс» и «Google»
Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
cетевое издание
issn 2310-6018

Обзор подходов к детектированию дефектов элементов ЛЭП на изображениях в инфракрасном, ультрафиолетовом и видимом спектрах

idАстапова М.А., Лебедев И.В. 

УДК 004.932; 621.3.051
DOI: 10.26102/2310-6018/2020.31.4.036

  • Аннотация
  • Список литературы
  • Об авторах

В работе представлен обзор современных методов мониторинга состояния элементов конструкции линий электропередач (ЛЭП) посредством обработки изображений в инфракрасном, ультрафиолетовом и видимом спектрах. Рассмотрены методы распознавания основных элементов конструкции ЛЭП и детектирования наиболее характерных для них дефектов, основанные на определении отличительных признаков (цвет, форма, границы, градиент яркости и текстура). В качестве основных элементов ЛЭП были рассмотрены изоляторы, провода, опоры и арматура. Анализ эффективности рассмотренных методов и подходов проводился на основе сравнения представленных в источниках метрик: значений доли верных распознаваний (accuracy), точности (precision) и полноты (recall). Особый интерес представляет анализ методов мониторинга элементов конструкции ЛЭП на основе изображений, полученных не только в видимом, но также в ультрафиолетовом и инфракрасном спектрах. Методы, предназначенные для обработки изображений в видимом спектре, основываются на алгоритмах глубокого и машинного обучения. Ультрафиолетовый спектр (УФ) используется для выявления коронных разрядов на проводах и изоляторах. Съемка в инфракрасном спектре (ИК) позволяет выявить дефекты элементов ЛЭП, которые не могут быть детектированы на изображениях в видимом спектре, например, горячие точки (hotspot). В результате проведенного анализа были рассмотрены методы детектирования дефектов ЛЭП. Методы с наибольшей эффективностью для видимого спектра: GVN, HOG + SVM, SSD, Grab cut, cascading CNN, LBP-HF + SVM, DMNN, VGG - 19, LBP + ULBP, YOLO v3, DELM + LRF, SVM, Faster R - CNN, CNN, стереозрение + PLAMEC. Методом детектирования с наибольшей эффективностью для ИК-спектра является «оцу + пороговая обработка», а для УФ-спектра метод – SVR.

1. Кудряков А.Г., Сазыкин В.Г., Кравченко И.И. Способ повышения надёжности воздушных линий электропередачи. Успехи Современной Науки. 2016;2(10):73-75.

2. Colak I., Sagiroglu S., Fulli G., Yesilbudak M., Covrig C.-F. A survey on the critical issues in smart grid technologies. Renewable and Sustainable Energy Reviews. 2016;54:396-405.

3. Peng F.Z. Flexible AC transmission systems (FACTS) and resilient AC distribution systems (RACDS) in smart grid. Proceedings of the IEEE. 2017;105(11):2099-2115.

4. Zormpas A., Moirogiorgou K., Kalaitzakis K., Plokamakis G.A., Partsinevelos P., Giakos G., Zervakis M. Power transmission lines inspection using properly equipped unmanned aerial vehicle (UAV). 2018 IEEE International Conference on Imaging Systems and Techniques (IST). 2018;1-5.

5. Tragulnuch P., Kasetkasem T., Isshiki T., Chanvimaluang T., Ingprasert S. High voltage transmission tower identification in an aerial video sequence using object-based image classification with geometry information. 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). 2018;473-476.

6. Bian J., Hui X., Zhao X., Tan M. A novel monocular-based navigation approach for UAV autonomous transmission-line inspection. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2018;1-7.

7. Menendez O., Cheein F.A.A., Perez M., Kouro S. Robotics in power systems: Enabling a more reliable and safe grid. IEEE Industrial Electronics Magazine. 2017;11(2):22-34.

8. Nguyen P., Dudkin S., Kong C. Automatic diagnostic of transmission lines based on ultraviolet inspection. E3S Web of Conferences. 2019;140:07008.

9. Juergen B., Fernando P.L., Thomas L. OCM 2017 - Optical Characterization of Materials - conference proceedings. KIT Scientific Publishing. 2017;248.

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

11. Шабанова А.Р., Толстой М.И., Лебедев И.В. Способ построения безопасных траекторий движения беспилотного летательного аппарата при мониторинге линий электропередач в условиях влияния электромагнитных полей. Проблемы региональной энергетики. 2019;3(44).

12. Князь В.А., Вишняков Б.В., Визильтер Ю.В., Горбацевич В.С., Выголов О.В. Технологии интеллектуальной обработки информации для задач навигации и управления беспилотными летательными аппаратами. Труды СПИИРАН. 2016;2(45):26-44.

13. Han Y., Liu Z., Lee D., Liu W., Chen J., Han Z. Han Y. et al. Computer vision–based automatic rod-insulator defect detection in high-speed railway catenary system. International Journal of Advanced Robotic Systems. 2018;15(3):1729881418773943.

14. Han J., Yang Z., Zhang Q., Chen C., Li H., Lai S., Hu G., Xu C., Xu H., Wang D., Chen R. A method of insulator faults detection in aerial images for high-voltage transmission lines inspection. Applied Sciences. 2019;9(10):2009.

15. Oberweger M., Wendel A., Bischof H. Visual recognition and fault detection for power line insulators. 19th computer vision winter workshop. 2014;1-8.

16. Tao X., Zhang D., Wang Z., Liu X., Zhang H., Xu D. Detection of power line insulator defects using aerial images analyzed with convolutional neural networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2018;1-13.

17. Bian J., Hui X., Zhao X., Tan M. A monocular vision–based perception approach for unmanned aerial vehicle close proximity transmission tower inspection. Int J Adv Robot Syst. 2019 Jan 1;16(1):1729881418820227.

18. Gong X., Yao Q., Wang M., Lin Y. A deep learning approach for oriented electrical equipment detection in thermal images. IEEE Access. 2018;6:41590-41597.

19. Zhai Y., Wang D., Zhang M., Wang J., Guo F. Fault detection of insulator based on saliency and adaptive morphology. Multimedia Tools and Applications. 2017;76(9):12051- 12064.

20. Wang X, Guo K, Wang Y. Detection algorithm of cracked insulator based on statistical shape models. Comput. Meas. Control. 2018;26:26-28.

21. Yin J., Lu Y., Gong Z., Jiang Y., Yao J. Edge detection of high-voltage porcelain insulators in infrared image using dual parity morphological gradients. IEEE Access. 2019;7:32728- 32734.

22. Zhang J., Liu L., Wang B., Chen X., Wang Q., Zheng T. High speed automatic power line detection and tracking for a UAV-based inspection. 2012 International Conference on Industrial Control and Electronics Engineering. 2012;266-269.

23. Tomaszewski M., Osuchowski J., Debita Ł. Effect of spatial filtering on object detection with the surf algorithm. International Scientific Conference BCI 2018 Opole. Springer, Cham. 2018;121-140.

24. Liao S., An J. A robust insulator detection algorithm based on local features and spatial orders for aerial images. IEEE Geoscience and Remote Sensing Letters. 2014;12(5):963- 967.

25. Cheng H., Zhai Y., Chen R., Wang D., Dong Z., Wang Y. Self-Shattering defect detection of glass insulators based on spatial features. Energies. 2019;12(3):543.

26. Ke H., Wang H., Li B. Image Segmentation Method of Insulator in Transmission Line Based on Weighted Variable Fuzzy C-Means. Journal of Engineering Science & Technology Review. 2017;10(4):115–123.

27. Guo L., Liao Y., Yao H., Chen J., Wang M. An electrical insulator defects detection method combined human receptive field model. Journal of Control Science and Engineering. 2018;1-9.

28. Dalal N., Triggs B. Histograms of oriented gradients for human detection. 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05). 2005;1:886-893.

29. Felzenszwalb P.F., Girshick R.B., McAllester D., Ramanan D. Object detection with discriminatively trained part-based models. IEEE transactions on pattern analysis and machine intelligence. 2009;32(9):1627–1645.

30. 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.

31. Hao-ran J, Lin-jun J, Shu-jia Y. Recognition and fault diagnosis of insulator string in aerial images. Journal of Mechanical & Electrical Engineering. 2015;32(2).

32. Otsu N. A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics. 1979;9(1):62-66.

33. Mallat S., Hwang W.L. Singularity detection and processing with wavelets. IEEE transactions on information theory. 1992;38(2):617-643.

34. Jabid T., Ahsan T. Insulator detection and defect classification using rotation invariant local directional pattern. Int. J. Adv. Comput. Sci. Appl. 2018;9(2):265-272.

35. Jabid T., Uddin M.Z. Rotation invariant power line insulator detection using local directional pattern and support vector machine. 2016 International Conference on Innovations in Science, Engineering and Technology (ICISET). 2016;1–4.

36. Jiang Y. T., Han J., Ding J. The identification and diagnosis of self-blast defects of glass insulators based on multi-feature fusion. Electr. Power. 2015;50(5):52-58.

37. Zhai Y., Wang D., Guo Y., Zhang M., Liu Y. Recognition of Aerial Insulator Image Based on Structural Model and the Optimal Entropy Threshold Segmentation. DEStech Transactions on Engineering and Technology Research. 2016;iceta.

38. Tiantian Y., Guodong Y., Junzhi Y. Feature fusion based insulator detection for aerial inspection. 2017 36th Chinese Control Conference (CCC). 2017;10972-10977.

39. Zuo D., Hu H., Qian R., Liu Z. An insulator defect detection algorithm based on computer vision. 2017 IEEE International Conference on Information and Automation (ICIA). 2017;361-365.

40. He K., Gkioxari G., Dollar P., Girshick R. Mask R-CNN. Proceedings of the IEEE international conference on computer vision. 2017;2961–2969.

41. Girshick R. Fast R-CNN. Proceedings of the IEEE international conference on computer vision. 2015;1440–1448.

42. Ren S., He K., Girshick R., Sun J. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence. 2016;39(6):1137–1149.

43. Redmon J., Divvala S., Girshick R., Farhadi A. You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016;779-788.

44. Pál D., Póczos B., Szepesvári C. Estimation of Rényi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs. Advances in neural information processing systems. 2010;1849-1857.

45. Liu W., Anguelov D., Erhan D., Szegedy C., Reed S., Fu C.-Y., Berg A.C. SSD: Single shot multibox detector. European conference on computer vision. Springer, Cham. 2016;21-37.

46. Simonyan K., Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.2014.

47. Szegedy C., Ioffe S., Vanhoucke V., Alemi A.A. Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence. 2017;31(1).

48. Yang Y., Wang L. Insulator recognition based on convolution neural network. MATEC Web of Conferences. 2017;139:00035.

49. Tomaszewski M., Michalski P., Ruszczak B., Zator S. Detection of power line insulators on digital images with the use of laser spots. IET Image Processing. 2019;13(12):2358- 2366.

50. Ma L., Xu C., Zuo G., Bo B., Tao F. Detection method of insulator based on faster r-cnn. 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). 2017. p. 1410–1414.

51. Zeiler M.D., Fergus R. Visualizing and understanding convolutional networks. European conference on computer vision. Springer, Cham. 2014;818-833.

52. Kang G., Gao S., Yu L., Zhang D. Deep architecture for high-speed railway insulator surface defect detection: Denoising autoencoder with multitask learning. IEEE Transactions on Instrumentation and Measurement. 2018;68(8):2679-2690.

53. Ling Z., Qiu R.C., Jin Z., Zhang Y., He X., Liu H., Chu L. An accurate and real-time selfblast glass insulator location method based on faster R-CNN and U-net with aerial images. arXiv preprint arXiv:1801.05143. 2018.

54. Castellucci P.B., Lucca L.C., SantAnna M., Traballe G., Mustacio V.H., da Silva J.F.R., Vallin S. Pole and crossarm identification in distribution power line images. 2013 Latin American Robotics Symposium and Competition. 2013;2-7.

55. Tragulnuch P., Chanvimaluang T., Kasetkasem T., Ingprasert S., Isshiki T. High Voltage Transmission Tower Detection and Tracking in Aerial Video Sequence using Object-Based Image Classification. 2018 International Conference on Embedded Systems and Intelligent Technology & International Conference on Information and Communication Technology for Embedded Systems (ICESIT-ICICTES). 2018;1-4.

56. Han B., Wang X. Learning for tower detection of power line inspection. DEStech Transactions on Computer Science and Engineering. 2016;iccae.

57. Han B., Wang X. Detection for power line inspection. MATEC Web of Conferences. EDP Sciences. 2017;100:03010.

58. Friedman J., Hastie T., Tibshirani R. Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The annals of statistics. 2000;28(2):337-407.

59. Martinez C., Sampedro C., Chauhan A., Collumeau J.F., Campoy P. The Power Line Inspection Software (PoLIS): A versatile system for automating power line inspection. Engineering applications of artificial intelligence. 2018;71:293-314.

60. Chen B., Miao X. Distribution Line Pole Detection and Counting Based on YOLO Using UAV Inspection Line Video. J Electr Eng Technol. 2020 Jan 1;15(1):441–8.

61. Santos T., Moreira M., Almeida J., Dias A., Martins A., Dinis J., Formiga J., Silva E. PLineD: Vision-based power lines detection for Unmanned Aerial Vehicles. 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). 2017;253–259.

62. Zhou G., Yuan J., Yen I., Bastani F. Robust real-time UAV based power line detection and tracking. 2016 IEEE International Conference on Image Processing (ICIP). 2016;744-748.

63. Chang W., Yang G., Li E., Liang Z. Toward a Cluttered Environment for Learning-Based Multi-Scale Overhead Ground Wire Recognition. Neural Processing Letters. 2018;48(3):1789-1800.

64. Liu Y., Li J., Xu W., Liu M. A method on recognizing transmission line structure based on multi-level perception. International Conference on Image and Graphics. Springer, Cham. 2017;512-522.

65. Jin L. J., Yan S. J., Liu Y. Vibration damper recognition based on Haar-like features and cascade AdaBoost classifier. Journal of System Simulation. 2012;24(09):1806-1809.

66. Wang W., Tian B., Liu Y., Li, J. Study on the electrical devices detection in UAV images based on region based convolutional neural networks. Journal of Geo-information Science. 2017;19(2):256-263.

67. Li S., Zhou H., Wang G., Zhu X., Kong L., Hu Z. Cracked insulator detection based on RFCN. Journal of Physics: Conference Series. 2018; 1069(1):012147.

68. Prasad P.S., Rao B.P. LBP-HF features and machine learning applied for automated monitoring of insulators for overhead power distribution lines. 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). 2016;808-812.

69. Prates R.M., Cruz R., Marotta A.P., Ramos R.P., Simas Filho E.F., Cardoso J.S. Insulator visual non-conformity detection in overhead power distribution lines using deep learning. Computers & Electrical Engineering. 2019;78:343–355.

70. Wan S., Liang Y., Zhang Y. Deep convolutional neural networks for diabetic retinopathy detection by image classification. Computers & Electrical Engineering. 2018;72:274-282.

71. Szegedy C., Wei Liu, Yangqing Jia, Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A. Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition. 2015;1–9.

72. Wu Z., Shen C., van den Hengel A. Wider or deeper: Revisiting the resnet model for visual recognition. Pattern Recognition. 2019;90:119–133.

73. Yang L., Jiang X., Hao Y., Li L., Li H., Li R., Luo B. Recognition of natural ice types on in-service glass insulators based on texture feature descriptor. IEEE Transactions on Dielectrics and Electrical Insulation. 2017;24(1):535-542.

74. Hao Y., Wei J., Jiang X., Yang L., Li L., Wang J., Li H., Li R. Icing condition assessment of in-service glass insulators based on graphical shed spacing and graphical shed overhang. Energies. 2018;11(2):318.

75. Maeda K., Takahashi S., Ogawa T., Haseyama M. Automatic estimation of deterioration level on transmission towers via deep extreme learning machine based on local receptive field. 2017 IEEE International Conference on Image Processing (ICIP). 2017;2379-2383.

76. Maeda K., Takahashi S., Ogawa T., Haseyama M. Estimation of deterioration levels of transmission towers via deep learning maximizing canonical correlation between heterogeneous features. IEEE Journal of Selected Topics in Signal Processing. 2018;12(4):633-644.

77. Lu J., Xu X., Li X., Li L., Chang C., Feng X., Zhang S. Detection of bird’s nest in high power lines in the vicinity of remote campus based on combination features and cascade classifier. IEEE Access. 2018;6:39063-39071.

78. Liu K. P., Wang B. H., Chen X. G., Jin L. J. Damaged cables recognition based on improved Freeman rule. Jidian Gongcheng/ Mechanical & Electrical Engineering Magazine. 2012;29(2):211-214.

79. Zhang Y., Huang X., Jia J., Liu X. A recognition technology of transmission lines conductor break and surface damage based on aerial image. IEEE Access. 2019;7:59022- 59036.

80. Mao T., Ren L., Yuan F., Li C., Zhang L., Zhang M., Chen Y. Defect recognition method based on HOG and SVM for drone inspection images of power transmission line. 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS). 2019;254–257.

81. Wang B., Wu R., Zheng Z., Zhang W., Guo J. Study on the method of transmission line foreign body detection based on deep learning. 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2). 2017;1-5.

82. Song Y., Wang L., Jiang Y., Wang H., Jiang W., Wang C., Chu J., Han D. A vision-based method for the broken spacer detection. 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). 2015;715–719.

83. Tang Y., Han J., Wei, W. Research on part recognition and defect detection of transmission line in deep learning. Electronic Measurement Technology. 2018;41(6):60-65.

84. Wang Z. Applied research on deep learning in defect detection of key components on transmission towers. Master’s thesis, Civil Aviation University of China. 2018.

85. Li Q., Ma Y., He F., Xi S., Xu J. Bionic vision-based intelligent power line inspection system. Computational and mathematical methods in medicine. 2017;2017:4964287.

86. Zhang Y., Yuan X., Li W., Chen S. Automatic power line inspection using UAV images. Remote Sensing. 2017;9(8):824.

87. Grum F., Costa L. F. Spectral emission of corona discharges. Applied Optics. 1976;15(1):76-79.

88. Lindner M., Elstein S., Lindner P., Topaz J.M., Phillips A. J. Daylight corona discharge imager. Eleventh International Symposium on High Voltage Engineering. 1999;4:349-352.

89. Komar G., Pischler O., Schichler U., Vieriu R.-L. Performance of UV and IR Sensors for Inspections of Power Equipment. Proceedings of the Nordic Insulation Symposium. 2019;26:82-87.

90. Jianwen Ding, Xiang Li, Xi Zhu, Xun Cao, Feng Yan, Bian X. Solar-irradiated leakage of UV camera for daytime corona inspection. 2015 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP). 2015;298-301.

91. Li J., Zhou Y., Yi X., Zhang M., Chen X., Cui M., Yan F. An Automatic Corona-discharge Detection System for Railways Based on Solar-blind Ultraviolet Detection. Current Optics and Photonics. 2017;1(3):196-202.

92. Duda R.O., Hart P.E. Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM. 1972;15(1):11-15.

93. Zhou W., Li H., Yi X., Tu J., Yu J. A criterion for UV detection of AC corona inception in a rod-plane air gap. IEEE Transactions on Dielectrics and Electrical Insulation. 2011;18(1):232-237.

94. Lv F., Dai R., Li H., Jin H. Comparison of Two UV Imaging Parameters’s in the Insulator Fault Diagnosis. 2012 Second International Conference on Intelligent System Design and Engineering Application. 2012;1400-1403.

95. Chen Q., Li Y., Yang G., Jin T., Zhang Z., Zhang S. Detection and Analysis of Ultraviolet Corona Discharge for Earth Switch Grading Ring. 2019 IEEE International Conference on Computational Electromagnetics (ICCEM). 2019;1-3.

96. Li X., Jin L., Xu Z., Jiang T., Jin H. Surface discharge detection method of contaminated insulators based on ultraviolet images’ parameters. 2017 1st International Conference on Electrical Materials and Power Equipment (ICEMPE). 2017;155-158.

97. Anbalagan S., Sudhakar T.D. Protection of Power Transmission Lines Using Intelligent Hot Spot Detection. 2019 Fifth International Conference on Electrical Energy Systems (ICEES). 2019;1-6.

98. Zaripova A.D., Zaripov D.K., Usachev A.E. Automatic condition monitoring method to find defects in high-voltage insulators using infrared images. E3S Web of Conferences. 2019;124:03003.

99. Wronkowicz A. Approach to automated hot spot detection using image processing for thermographic inspections of power transmission lines. Diagnostyka. 2016;17.

100. Zhao Z., Xu G., Qi Y. Representation of binary feature pooling for detection of insulator strings in infrared images. IEEE Transactions on Dielectrics and Electrical Insulation. 2016;23(5):2858-2866.

Астапова Марина Алексеевна

Email: marinaastapova55@gmail.com

ORCID |

Санкт-Петербургский Федеральный исследовательский центр Российской академии наук (СПб ФИЦ РАН)
Санкт-Петербургский институт информатики и автоматизации Российской академии наук

Санкт-Петербург, Российская Федерация

Лебедев Игорь Владимирович

Email: igorlevedev@gmail.com

Санкт-Петербургский Федеральный исследовательский центр Российской академии наук (СПб ФИЦ РАН)
Санкт-Петербургский институт информатики и автоматизации Российской академии наук

Санкт-Петербург, Российская Федерация

Ключевые слова: беспилотный летательный аппарат, обследование высоковольтных линий электропередач, обнаружение неисправностей, определение дефектов, спектральный анализ изображений

Для цитирования: Астапова М.А., Лебедев И.В. Обзор подходов к детектированию дефектов элементов ЛЭП на изображениях в инфракрасном, ультрафиолетовом и видимом спектрах. Моделирование, оптимизация и информационные технологии. 2020;8(4). URL: https://moitvivt.ru/ru/journal/pdf?id=883 DOI: 10.26102/2310-6018/2020.31.4.036

1640

Полный текст статьи в PDF

Опубликована 31.12.2020