Unmanned aerial vehicle transmission defect detection technology based on edge computing
1
Fangchenggang Power Supply Bureau of Guangxi Power Grid Co., Ltd, Fangchenggang, 538001, China
Submission date: 2024-05-28
Final revision date: 2024-08-15
Acceptance date: 2024-10-14
Online publication date: 2024-10-27
Publication date: 2024-10-27
Corresponding author
Xi Chen
Fangchenggang Power Supply Bureau of Guangxi Power Grid Co., Ltd, Fangchenggang, 538001, China
Fangchenggang Power Supply Bureau of Guangxi Power Grid Co., Ltd, Fangchenggang, 538001, China
Diagnostyka 2024;25(4):2024408
KEYWORDS
TOPICS
Diagnostic systemsTechnical diagnosticsDiagnostics of electronic systemsDiagnostics of electrical and energy machines
ABSTRACT
Defect identification of transmission lines has become a crucial step in ensuring the proper functioning of the current transmission system due to the ongoing growth of the power grid size.The study primarily concerns itself with the current shortcomings of unmanned aerial vehicle transmission defect detection, particularly in terms of image quality and other related issues. In response, an unmanned aerial vehicle transmission defect detection system based on edge computing has been proposed.This system employs edge computing networks and lightweight improvements,and finally, through the analysis of experimental data,the performance and detection effectiveness of the system are validated.The outcomes revealed that the accuracy of the model used for the study in detecting insulators is 0.05 higher than other models.The system was more effective in detecting normal insulators and abnormal insulators.The error of the system in detecting transmission line images was 0.18 lower than other algorithms, and the average percentage error was 0.20 lower compared to other model error values. This reveals that the system used in the study was able to improve the detection of transmission lines,and also improved the quality of the detected images. This is an excellent manual for enhancing UAV transmission line defect detection precision in the future.
FUNDING
The research is supported by: Guangxi Power Grid Co., Ltd. Science and Technology Project Funding, "Research on Online Defect Identification Technology for Mobile Edge End", (No. 040700KC23040002)
REFERENCES (25)
1.
Nguyen DC, Tam LN, Phan DH, Nguyen TC, Duy DN, Xuan QN. Using drone and ai application for power transmission line inspection and maintenance: A case study in vietnam. Intelligent Computing 2023; 711; 684–98. https://doi.org/10.1007/978-3-....
2.
Singh G, Stefenon SF, Yow KC. Interpretable visual transmission lines inspections using pseudo-prototypical part network. Machine Vision and Applications 2023; 34(3): 41. https://doi.org/10.1007/s00138....
3.
Shafi I, Mazhar MF, Fatima A, Alvarez RM, Miró Y, Espinosa JCM, et al. deep learning-based real time defect detection for optimization of aircraft manufacturing and control performance. Drones 2023; 7(1):31. https://doi.org/10.3390/drones....
4.
Seo D, Kim J, Park S. An Experimental Study on defect detection of anchor bolts using non-destructive testing techniques. Materials 2023; 16(13): 4861. https://doi.org/10.3390/ma1613....
5.
Luo P, Wang B, Wang H, Ma F, Ma H, Wang L. An ultrasmall bolt defect detection method for transmission line inspection. IEEE Transactions on Instrumentation and Measurement 2023; 72: 1–12. https://doi.org/10.1109/TIM.20....
6.
Dong K, Shen Q, Wang C, Dong Y, Liu Q, Lu Z, et al. Improved swin transformer-based defect detection method for transmission line patrol inspection images. Evolutionary Intelligence 2023. https://doi.org/10.1007/s12065....
7.
Liu Y, Liu D, Huang X, Li C. Insulator defect detection with deep learning: A survey. IET Generation, Transmission & Distribution 2023; 17(16): 3541–58. https://doi.org/10.1049/gtd2.1....
8.
Wu Y, Cao H, Yang G, Lu T, Wan S. Digital twin of intelligent small surface defect detection with cyber-manufacturing systems. ACM Transactions on Internet Technology 2023; 23(4): 1–20. https://doi.org/10.1145/357173....
9.
Han G, Yuan Q, Zhao F, Wang R, Zhao L, Li S, et al. An improved algorithm for insulator and defect detection based on YOLOv4. Electronics 2023; 12(4): 933. https://doi.org/10.3390/electr....
10.
Manaz H F, Banu R N, BK, A OP. transmission line quality inspection using ai. 2024 third international conference on intelligent techniques in control, Optimization and Signal Processing (INCOS) 2024; 1–5. https://doi.org/10.1109/INCOS5....
11.
Ahmed MdF, Mohanta JC. Autonomous site inspection of power transmission line insulators with unmanned aerial vehicle system. Electric Power Components and Systems 2024: 1–24. https://doi.org/10.1080/153250....
12.
Benelmostafa BE, Aitelhaj R, Elmoufid M, Medromi H. Detecting broken glass insulators for automated UAV power line inspection based on an improved YOLOv8 model. International Conference on Advanced Intelligent Systems for Sustainable Development 2023; 15(11): 309-321. https://doi.org/10.1080/153250....
13.
Mokhtar MIM, Basri AA, Basri EI, Sultan MTH. Defect detection image processing for drone inspection on wide-body aircraft surface. E3S Web of Conferences 2024; 477: 00100. https://doi.org/10.1051/e3scon....
14.
Saberironaghi A, Ren J, El-Gindy M. Defect detection methods for industrial products using deep learning techniques: A review. Algorithms 2023; 16(2): 95. https://doi.org/10.3390/a16020....
15.
Chatzargyros G, Papakonstantinou A, Kotoula V, Stimoniaris D, Tsiamitros D. UAV Inspections of power transmission networks with ai technology: a case study of lesvos Island in Greece. Energies 2024; 17(14): 3518. https://doi.org/10.3390/en1714....
16.
Souza BJ, Stefenon SF, Singh G, Freire RZ. Hybrid-YOLO for classification of insulators defects in transmission lines based on UAV. International Journal of Electrical Power & Energy Systems 2023; 148: 108982. https://doi.org/10.1016/j.ijep....
17.
Syu JH, Lin JCW, Srivastava G, Yu K. A comprehensive survey on artificial intelligence empowered edge computing on consumer electronics. IEEE Transactions on Consumer Electronics 2023;69(4):1023–34. https://doi.org/10.1109/TCE.20....
18.
Hussain M. YOLO-v1 to YOLO-v8, the Rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection. Machines 2023; 11(7): 677. https://doi.org/10.3390/machin....
19.
Bhuiyan MR, Uddin J. Deep transfer learning models for industrial fault diagnosis using vibration and acoustic sensors data: A review. Vibration 2023; 6(1): 218–38. https://doi.org/10.3390/vibrat....
20.
Silitonga D, Rohmayanti SAA, Aripin Z, Kuswandi D, Sulistyo AB, Juhari. Edge Computing in E-commerce Business: Economic Impacts and Advantages of Scalable Information Systems. ICST Transactions on Scalable Information Systems 2023;11(1) https://doi.org/10.4108/eetsis....
21.
Bourechak A, Zedadra O, Kouahla MN, Guerrieri A, Seridi H, Fortino G. At the confluence of artificial intelligence and edge computing in iot-based applications: A review and new perspectives. Sensors 2023; 23(3): 1639. https://doi.org/10.3390/s23031....
22.
Pal S, Roy A, Shivakumara P, Pal U. Adapting a swin transformer for license plate number and text detection in drone images. Artificial Intelligence and Applications 2023; 1(3): 145–54. https://doi.org/10.47852/bonvi....
23.
Raeisi-Varzaneh M, Dakkak O, Habbal A, Kim BS. Resource scheduling in edge computing: architecture, taxonomy, Open Issues and Future Research Directions. IEEE Access 2023; 11: 25329–50. https://doi.org/10.1109/ACCESS....
24.
Raith P, Nastic S, Dustdar S. Serverless edge computing—where we are and what lies ahead. IEEE Internet Computing 2023; 27(3): 50–64. https://doi.org/10.1109/MIC.20....
25.
Alnaim AK, Alwakeel AM. Machine-learning-based iot–edge computing healthcare solutions. Electronics 2023;12(4):1027. https://doi.org/10.3390/electr....
| eISSN: | 2449-5220 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
