Research on foreign body detection in transmission lines based on a multi-UAV cooperative system and YOLOV7

Research on foreign body detection in transmission lines based on a multi-UAV cooperative system and YOLOV7

Автор: Chang R., Mao Zh., Hu J., Bai H., Zhou Ch., Yang Ya., Gao Sh.

Журнал: Компьютерная оптика @computer-optics

Рубрика: Обработка изображений, распознавание образов

Статья в выпуске: 5 т.47, 2023 года.

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The unique plateau geographical features and variable weather of Yunnan, China make transmission lines in this region more susceptible to coverage and damage by various foreign bodies compared to flat areas. The mountainous terrain also presents great challenges for inspecting and removing such objects. In order to improve the efficiency and detection accuracy of foreign body inspection of transmission lines, we propose a multi-UAV collaborative system specifically designed for the geographical characteristics of Yunnan's transmission lines in this paper. Additionally, the image data of foreign bodies was augmented, and the YOLOv7 target detection model, which offers a more balanced trade-off between precision and speed, was adopted to improve the accuracy and speed of foreign body detection.

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Object-detection, multi-uav, yolov7, transmission-lines

Короткий адрес: https://sciup.org/140301858

IDR: 140301858   |   DOI: 10.18287/2412-6179-CO-1257

Список литературы Research on foreign body detection in transmission lines based on a multi-UAV cooperative system and YOLOV7

  • Liu S, Yang Q, Cai H, Yan M, Zhang M, Wu D, Xie M. Market reform of Yunnan electricity in southwestern China: Practice, challenges and implications. Renew Sust En-erg Rev 2019; 113: 109265. DOI: \0.\0\6/j.rser.20\9.\09265.
  • Manfreda S, McCabe MF, Miller PE, Lucas R, Madrigal VP, Mallinis G, Ben Dor E, Helman D, Estes L, Ciraolo G, Mullerova J, Tauro F, De Lima MI, De Lima JLMP, Maltese A, Frances F, Caylor K, Kohv M, Perks M, Ruiz-Perez G, Su Z, Vico G, Toth B. On the use of unmanned aerial systems for environmental monitoring. Remote Sens 2018; 10(4): 641. DOI: 10.3390/rs10040641.
  • Ventura D, Bonifazi A, Gravina MF, Belluscio A, Ardiz-zone G. Mapping and classification of ecologically sensitive marine habitats using unmanned aerial vehicle (UAV) imagery and object-based image analysis (OBIA). Remote Sens2018; 10(9): 1331. DOI: 10.3390/rs10091331.
  • Rusnak M, Sladek J, Kidova A, Lehotsky M. Template for high-resolution river landscape mapping using UAV technology. Measurement 2018; 115: 139-151. DOI: 10.1016/j.measurement.2017.10.023.
  • Langhammer J, Vackova T. Detection and mapping of the geomorphic effects of flooding using UAV photogramme-try. Pure Appl Geophys 2018; 175: 3223-3245. DOI: 10.1007/s00024-018-1874-1.
  • James MR, Chandler JH, Eltner A, Fraser C, Miller PE, Mills JP, Noble T, Robson S, Lane SN. Guidelines on the use of structure-from-motion photogrammetry in geo-morphic research. Earth Surf Process Landf 2019; 44: 2081-2084. DOI: 10.1002/esp.4637.
  • Dyson J, Mancini A, Frontoni E, Zingaretti P. Deep learning for soil and crop segmentation from remotely sensed data. Remote Sens 2019; 11(16): 1859. DOI: 10.3390/rs11161859.
  • Popescu D, Stoican F, Stamatescu G, Ichim L, Dragana C. Advanced UAV-WSN system for intelligent monitoring in precision agriculture. Sensors 2020; 20(3): 817. DOI: 10.3390/s20030817.
  • Constantin A, Dinculescu R-N. UAV development and impact in the power system. 2019 8th Int Conf on Modern Power Systems (MPS) 2019: 1-5. DOI: 10.1109/MPS.2019.8759745.
  • Rafique SF, Bodla MK, Ahmed Z, Nasir U, Zaidi A, Saleem M. Design and implementation of a UAV for power system utility inspection. 2014 16 th Int Power Electronics and Motion Control Conf and Exposition 2014: 11461150. DOI: 10.1109/EPEPEMC.2014.6980665.
  • Addabbo P, Angrisano A, Bernardi ML, Gagliarde G, Mennella A, Nisi M, Ullo SL. UAV system for photovoltaic plant inspection. IEEE Aerosp Electron Syst Mag 2018; 33: 58-67. DOI: 10.1109/MAES.2018.170145.
  • Li L. The UAV intelligent inspection of transmission lines, In Book: Proceedings of the international conference on advances in mechanical engineering and industrial informatics. Atlantis Press; 2015: 1542-1545. DOI: 10.2991/ameii-15.2015.285.
  • Toth J, Gilpin-Jackson A. Smart view for a smart grid -Unmanned Aerial Vehicles for transmission lines. 2010 1st Int Conf on Applied Robotics for the Power Industry 2010: 1-6. DOI: 10.1109/CARPI.2010.5624465.
  • Zormpas A, Moirogiorgou K, Kalaitzakis K, Plokamakis GA, Partsinevelos P, Giakos G, Zervakis M. Power transmission lines inspection using properly equipped unmanned aerial vehicle (UAV). 2018 IEEE Int Conf on Imaging Systems and Techniques (IST) 2018: 1-5. DOI: 10.1109/IST.2018.8577142.
  • He T, Zeng Y, Hu Z. Research of multi-rotor UAVs detailed autonomous inspection technology of transmission lines based on route planning. IEEE Access 2019; 7: 114955-114965. DOI: 10.1109/ACCESS.2019.2935551.
  • Guo S, Bai Q, Zhou X. Foreign object detection of transmission lines based on faster R-CNN. In Book: Kim KJ, Kim H-Y, eds. Information science and applications, Singapore: Springer Nature Singapore Pte Ltd; 2020: 269-275. DOI: 10.1007/978-981-15-1465-4_28.
  • Yao N, Zhu L. A novel foreign object detection algorithm based on GMM and K-means for power transmission line inspection. J Phys Conf Ser 2020; 1607: 012014. DOI: 10.1088/1742-6596/1607/1/012014.
  • Li J, Nie Y, Cui W, Liu R, Zheng Z. Power transmission line foreign object detection based on improved YOLOv3 and deployed to the chip. 2020 3rd Int Conf on Machine Learning and Machine Intelligence, Association for Computing Machinery 2020: 100-104. DOI: 10.1145/3426826.3426845.
  • Song Y, Zhou Z, Li Q, Chen Y, Xiang P, Yu Q, Zhang L, Lu Y. Intrusion detection of foreign objects in high-voltage lines based on YOLOv4. 2021 6th Int Conf on Intelligent Computing and Signal Processing (ICSP) 2021: 12951300. DOI: 10.1109/ICSP51882.2021.9408753.
  • Wang Q, Si G, Qu K, Gong J, Cui L. Transmission line foreign body fault detection using multi-feature fusion based on modified YOLOv5. J Phys Conf Ser 2022; 2320: 012028. DOI: 10.1088/1742-6596/2320/1/012028.
  • Xing L, Fan X, Dong Y, Xiong Z, Xing L, Yang Y, Bai H, Zhou C. Multi-UAV cooperative system for search and rescue based on YOLOv5. Int J Disaster Risk Reduct 2022; 76: 102972. DOI: 10.1016/j.ijdrr.2022.102972.
  • Wang C-Y, Bochkovskiy A, Liao H-YM. YOLOv7: Train-able bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv Preprint. 2022. Source: https://arxiv.org/abs/2207.02696. DOI: 10.48550/arXiv.2207.02696.
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