Intelligent detection of small sample transmission line defect edge based on convolutional neural networks

Xiongxin Mo; Zhenglun Chen; Honghao Cao; Yixin Sun; Qinghua Shao

doi:10.1117/12.3029854

21 June 2024 Intelligent detection of small sample transmission line defect edge based on convolutional neural networks

Xiongxin Mo, Zhenglun Chen, Honghao Cao, Yixin Sun, Qinghua Shao

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131673J (2024) https://doi.org/10.1117/12.3029854
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

With the continuous popularization of artificial intelligence, the establishment of transmission line image automatic detection system is imminent. Due to the small number of samples and high similarity of transmission line image data sets, traditional image detection algorithms cannot achieve real-time positioning and accurate identification of power equipment components. In view of the above problems, this paper proposes an intelligent recognition method of transmission line defect edge based on deep learning. Firstly, the convolutional neural network (CNN) is used to extract the feature of transmission line defect graphics, and a large number of sample data are obtained. Secondly, the edge algorithm is used to optimize the CNN algorithm to determine the pixels, and the transmission line defect detection is completed. Finally, it is applied to the self-built transmission line data set for verification. The average detection accuracy of this method is 89.2%, which has excellent detection accuracy and generalization ability.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiongxin Mo, Zhenglun Chen, Honghao Cao, Yixin Sun, and Qinghua Shao "Intelligent detection of small sample transmission line defect edge based on convolutional neural networks", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131673J (21 June 2024); https://doi.org/10.1117/12.3029854

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