Background: This paper presents an automatic instrument recognition method highlighting
the deep learning aspect of instrument identification in order to advance the automatic process
of video monitoring remotely equipment of substation.
Methodology: This work utilizes the Scale Invariant Feature Transform approach (SIFT) and the
Gaussian difference model for instrument positioning while proposing a design scheme of the instrument
Results: The experimental outcomes obtained proves that the proposed system is capable of automatically
recognizing a modest graphical interface and study independently while improving the
effectiveness of the appliance, thereby, realizing the purpose of spontaneous self-check. The proposed
approach is applicable for musical instrument recognition, and it provides 92% of the accuracy
rate, 87.5% precision value and recall rate of 91.2%.
Conclusion: The comparative analysis with other state-of-the-art methods justifies that the proposed
deep learning-based music recognition method outperforms the other existing approaches in
terms of accuracy, thereby providing a practicable music instrument recognition solution.