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16 December 2022 Research on convolutional neural network method for transmission line abnormal sound identification
Fudong Cai, Xiaobin Sun, Xiaoqing Liu, Jie Yang, Guoxin Guo, Zhiqiang Kong, Huanyun Liu, Changfeng Lv
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Proceedings Volume 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022); 125005K (2022) https://doi.org/10.1117/12.2660334
Event: 5th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 2022, Chongqing, China
Abstract
At present, most of the hidden troubles in transmission lines are detected by image and video technology, however, there are too many monitoring targets, due to the influence of weather changes and lighting, and other factors, it is difficult to accurately monitor the hidden troubles of transmission lines only by video images, this paper presents a convolutional neural network method to identify the abnormal sound in transmission lines. The audio data collected by the audio-visual acquisition device is processed by the convolutional neural network to identify the potential audio data, filter the non-potential audio data, and improve the speed of potential danger analysis. Through the training and testing of sound multi-label classification and detection algorithm, the validity of the method is verified.
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Fudong Cai, Xiaobin Sun, Xiaoqing Liu, Jie Yang, Guoxin Guo, Zhiqiang Kong, Huanyun Liu, and Changfeng Lv "Research on convolutional neural network method for transmission line abnormal sound identification", Proc. SPIE 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 125005K (16 December 2022); https://doi.org/10.1117/12.2660334
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KEYWORDS
Convolutional neural networks
Convolution
Detection and tracking algorithms
Acoustics
Speaker recognition
Speech recognition
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