Research on insulator self-explosion fault identification of UAV inspection based on machine learning

Sen Wang; Wanhong Yu; Wenshuo Wang; Haoming Qu; Nan Chen; Bingnan Zhao; Dianzhe Zhao; Yueyan Leng; Jiaze Wu

doi:10.1117/12.2675366

25 May 2023 Research on insulator self-explosion fault identification of UAV inspection based on machine learning

Sen Wang, Wanhong Yu, Wenshuo Wang, Haoming Qu, Nan Chen, Bingnan Zhao, Dianzhe Zhao, Yueyan Leng, Jiaze Wu

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126360I (2023) https://doi.org/10.1117/12.2675366
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

In the study of UAV detection of transmission line defects, in order to improve the accuracy of detecting insulator defects, a self-explosion insulator detection method based on optimized YOLOv5 is proposed. In the training, the activation function is optimized, and the original ReLU activation function is optimized to SiLU activation function. Through the training and verification of a large number of transmission line image data collected by UAV inspection, the experimental results show that this method can effectively detect the self-explosion and falling defects of glass insulators under various complex background conditions. The mean average accuracy is used to evaluate the method. The detection rate of insulator self-explosion and flake defect is 94.7 %.

Citation Download Citation

Sen Wang, Wanhong Yu, Wenshuo Wang, Haoming Qu, Nan Chen, Bingnan Zhao, Dianzhe Zhao, Yueyan Leng, and Jiaze Wu "Research on insulator self-explosion fault identification of UAV inspection based on machine learning", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126360I (25 May 2023); https://doi.org/10.1117/12.2675366

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