Research on Algorithm and System Simulation of UAV Power Inspection under Information Fusion

In this paper, a UAV power inspection system based on information fusion is proposed to improve the accuracy and efficiency of inspection. The system integrated use of multi-sensor data, including high-definition camera, infrared thermal imager and liDAR, combined with visual measurement algorithm, to achieve an all-round, multi-angle monitoring of power facilities. In the algorithm design, the paper introduced a deep learning model to improve the accuracy and speed of target recognition by training many power facility images. In addition, the system simulation part adopts advanced simulation software to build the virtual environment of power facilities and UA V inspection, and verifies the effectiveness of the algorithm and the stability of the system through accurate data analysis. The simulation results show that the information fusion algorithm can significantly improve the recall rate and accuracy rate of target detection, reaching 95% and 98%, respectively. Meanwhile, the inspection efficiency of UAV is increased by 30%. The research results of this paper provide a new solution for the intelligent inspection of electric power industry, which has important theoretical value and application prospect.