Analysis and Detection of Transmission Lines Based on Twin Reality

Abstract

Now life has been transformed and upgraded to the direction of intelligence, digitalization and informatization along with the advancement of scientific and technological information. The traditional detection of transmission lines is completed by the professional maintenance personnel of the power grid through manual detection of lines. This method is inefficient and has a certain threat to the life safety of detection personnel. The development of deep learning and computational vision supplies a fresh idea for detection of transmission lines. Therefore, this paper analyzes the traditional Faster R-CNN based on twin reality scene modeling technology. Then aiming at the shortcomings of traditional algorithms, the Cascade R-CNN algorithm is proposed to complete the detection of transmission lines based on twin reality system. It compares the accuracy and other indicators of Cascade R-CNN and Faster R-CNN algorithms. The effectiveness of two algorithms for analysis and detection of transmission lines is verified through experiments. Finally, the results indicates that the system using Cascade R-CNN has higher prediction accuracy, and has better practicability for analysis and detection of transmission lines.