Research on insulator online spraying system based on AI assistance of UAV front-end

Abstract Due to the low automation level and low working efficiency of the existing drone spraying technology, it is difficult to meet the needs of large-scale applications and diversified requirements. Therefore, this paper designs a CGSP detection model for insulator detection and an online optimization Kalman tracking algorithm based on the BP neural network, effectively improving detection efficiency and accuracy. An online coating system is established. Real-time analysis and adjustment of coating coverage and spraying uniformity are achieved through image differential analysis, color histogram, and Haralick texture feature algorithm, ensuring that the sprayed blocks meet the standard values. The experimental results show that the system implemented in this study realizes a semi-automatic closed-loop process of insulator target recognition, flight tracking, and online spraying assisted by artificial intelligence (AI).