Paper
5 June 2024 A state detection method for high voltage distribution network equipment based on improved YOLOv5
Qiwen Zhang, Kai Li, Jianfeng Guo, Peiqin Zhan, Junyong Cui
Author Affiliations +
Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131635D (2024) https://doi.org/10.1117/12.3030633
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China
Abstract
In this study, an improved YOLOv5 algorithm is proposed for the task of high-voltage distribution network equipment status detection, aiming to enhance the performance and flexibility of the detection network by introducing a decoupled detection head design. To achieve this, we collected a diverse dataset of high-voltage distribution network equipment, taking into account various influencing factors such as different states, environmental conditions, and shooting angles. Experimental results confirm that our model exhibits significant advantages in the performance of high-voltage distribution network equipment status detection compared to traditional models, especially in terms of accuracy and robustness, providing an efficient and accurate solution for the automation of high-voltage distribution network equipment in power systems.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Qiwen Zhang, Kai Li, Jianfeng Guo, Peiqin Zhan, and Junyong Cui "A state detection method for high voltage distribution network equipment based on improved YOLOv5", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131635D (5 June 2024); https://doi.org/10.1117/12.3030633
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KEYWORDS
Object detection

Head

Target detection

Circuit switching

Design

Performance modeling

Autoregressive models

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