Research on Intelligent Data Processing and Signal Processing Algorithm in UAV Power Inspection

In this article, an advanced data handling technique integrating Principal Component Analysis (PCA), the YOLOv5 methodology, and Fourier transformation is introduced for image manipulation and signal scrutiny in UAV power audits. Initially, PCA is employed to condense the image dataset acquired during UAV surveillance, eliminate superfluous details, and isolate essential characteristics. Subsequently, the YOLOv5 technique is utilized to pinpoint power infrastructure components, execute object localization and categorization, and discern possible malfunction patterns. Ultimately, the sensor readings are examined in the frequency domain via Fourier transformation to pinpoint irregular vibration frequencies and other anomalous signals. This approach can efficiently process and scrutinize extensive inspection datasets, as well as promptly and accurately spot latent defects in power installations, thereby enhancing the operational efficacy and safety of the power grid.