Hidden Danger Detection and Identification System of Power Transmission Tower Based on YOLOV11

Xin Zou; Yanru Hu

doi:10.54097/rs28p954

Authors

Xin Zou
Yanru Hu

DOI:

https://doi.org/10.54097/rs28p954

Keywords:

YOLOv11; Power pylons; A hidden danger; Target detection; GUI.

Abstract

This study proposed a hidden danger detection and identification system of power transmission tower based on YOLOv11, aiming to introduce an intelligent detection and identification system of hidden danger of power transmission tower based on YOLOv11 algorithm, which is committed to enhancing the safety and stability of power transmission tower. YOLOv11, the latest and most advanced object detection algorithm in the YOLO family, is known for its excellent detection efficiency and accuracy. In this system, YOLOv11 is specially trained to accurately identify four potential hazards on power pylons: bird's nests, balloons, garbage and kites. The accuracy of the system is as high as 93.8%, and the recall rate has reached an excellent level of 73.3%. In order to fully verify the actual utility of the model, we carefully built a user-friendly intuitive interface (UI) using PyQt5. The system integrates image detection, video detection and real-time camera monitoring and recognition functions, can accurately identify the above four types of hidden dangers, and comprehensively record all the detection results, which provides great convenience for subsequent data analysis and processing. This system is not only easy to operate, user-friendly user interface design, but also shows excellent performance in the process of real-time monitoring and recognition, ensuring a high degree of accuracy. These characteristics have laid a solid foundation for the subsequent related work.

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References

[1] Zhao et al. Detection Method of Fittings on Power Transmission Towers Based on Improved YOLOx-S [J]. Radio Engineering, 2023, 53(11):2664-2672.

[2] Hao et al. Fault Detection of Power Transmission Lines Based on YOLOv5 with Attention Mechanism and Cross-Scale Feature Fusion [J]. Proceedings of the CSEE, 2023. DOI: 10.13334/j.0258-8013.pcsee.212607.

[3] Yang et al. Lightweight YOLOv3-Based Detection Method for Bird Nests on Power Transmission Lines Using Depthwise Separable Convolutions [J]. Shaanxi Electric Power, 2021 (012):049.

[4] Shi et al. Target Detection Method, Device, and UAV for Power Transmission Lines Based on YOLOv3-tiny: CN202010450441.7 [P]. CN112287740A [2024-10-19].

[5] Du et al. An Abnormal Target Detection Method for Power Transmission Lines Based on Improved YOLOv3: CN202010206891.1 [P]. CN111444809A [2024-10-19].

[6] Sun et al. Bird Damage Detection Method for Power Transmission Lines Based on Artificial Intelligence [C]//2019 Annual Conference on Informatization in the Power Industry. [2024-10-19].

[7] Zhang et al. Research on Bird Nest Detection Method for Power Transmission Lines Based on Improved YOLOv5 [J]. Power System Protection and Control, 2023, 51(2):151-159. DOI: 10.19783/j.cnki.pspc.220428.

[8] Wang et al. Industrial Bar Material Recognition Algorithm Based on Improved YOLOv4 [J]. Machinery & Electronics, 2022(040-001).

[9] Shao et al. Overview of YOLO Target Detection Based on Deep Learning [J]. Journal of Electronics & Information Technology, 2022, 44(10):12. DOI: 10.11999/JEIT210790.

[10] Mi and Lian. Review of YOLO Methods for General Object Detection [J]. Computer Engineering and Applications, 2024 (7).

[11] Ning et al. Review of Target Detection Algorithms Based on Deep Learning [J]. Digital Design (Upper), 2022(005):000.

[12] Wu and Zhang. Review of Target Detection Algorithms Based on Deep Learning [J]. Information and Computer, 2019(12):3. DOI: CNKI:SUN:XXDL.0.2019-12-023.

[13] Zhang et al. Review of Target Detection Algorithms Based on Deep Learning [C]//Papers Collection of the 22nd Annual Conference on New Technologies and Applications of the Network of China Computer User Association Network Application Branch in 2018. 2018.

[14] Willman J. Overview of PyQt5 [J]. 2021. DOI: 10.1007/978-1-4842-6603-8_1.

[15] Sang and Guo. Design of a Digital Image Processing Experimental Platform Based on PyQt5 [J]. Electronic Technology & Software Engineering, 2021, 000(018):P.129-130.