Research on Recognition Algorithm of Power Facility Inspection Robot Based on Computer Vision

In the modern power grid maintenance and management, automation and intelligence have become the key factors to improve the safety and efficiency of power facilities. In this paper, a new recognition algorithm combining YOLOv5 target detection framework and Convolutional Block Attention Module (CBAM) is proposed, which enhances the sensitivity of the model to key areas, reduces the false alarm rate, maintains a high detection speed, and improves the intelligent perception ability of power facility inspection robots. The algorithm can not only quickly locate and identify various power equipment, but also automatically complete the preliminary diagnosis of equipment status and find potential faults in time, which opens up a new path for the automatic inspection and intelligent operation and maintenance of the power industry. Future work will focus on further optimizing the computational efficiency of the algorithm, and explore the application in a wider range of power facility types.