Sound Recognition of Bird Species Related to Power Grid Faults Based on Improved MobileViT Lightweight Network
Accurate recognition of bird species that are harmful to transmission lines is of great significance to the differ-entiated prevention and control of bird-involved faults in power grids. Currently, bird song recognition methods based on deep learning have been widely applied. However, existing recognition deep models have problems of a large number of parameters and thus low training efficiency. To address this issue, we introduce the MobileViT network, a lightweight and efficient model to identify bird species by their sounds. By incorporating the Squeeze Excitation (SE) mechanism into the MV2 module of the standard MobileViT model, we propose the MobileViT-SE model, an improved version of the MobileViT model with stronger recognition capability. We evaluate the recognition ability of both the MobileViT model and the MobileViT-SE model with au-dio samples of typical bird species that are harmful to transmission lines. Experimental results show that compared with other typical deep learning models, the MobileViT model and the MobileViT-SE model have higher average recognition accuracy while using fewer number of parameters. Specifically, the average test accuracy of the MobileViT model is 95.5％, and the av-erage test accuracy of the MobileViT-SE model is 97.2％, which is 1.7 percentage higher than the former.
bird-involved faults in power gridsbird song recognitionMobileViT-SE modelSqueeze Excitation mechanism
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