1. Kudryakov A.G., Sazykin V.G., Kravchenko I.I. Sposob povysheniya nadyozhnosti
vozdushnyh linij elektroperedachi. Uspekhi Sovremennoj Nauki. 2016;2(10):73-75. (In
Russ.).
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. Shabanova A.R., Tolstoj M. I., Lebedev I. V. Sposob postroeniya bezopasnyh traektorij
dvizheniya bespilotnogo letatel'nogo apparata pri monitoringe linij elektroperedach v
usloviyah vliyaniya elektromagnitnyh polej. Problemy Regional'noj Energetiki.
2019;3(44). (In Russ.).
12. Knyaz' V.A., Vishnyakov B.V., Vizil'ter YU.V., Gorbacevich V.S., Vygolov O.V.
Tekhnologii intellektual'noj obrabotki informacii dlya zadach navigacii i upravleniya
bespilotnymi letatel'nymi apparatami. Trudy SPIIRAN. 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.
