A Review of Intelligent Depth Distance Perception Research for Power Transmission Line Corridor Scenarios

Abstract

Transmission lines are essential carriers for the transmission of electrical energy, and their safe operation is fundamental to ensuring reliable power supply. However, the environment of transmission line corridors is complex and variable, posing numerous risks of external damage. With the development of smart power systems, information technologies such as video monitoring provide effective means for monitoring and managing external damage to transmission lines. Nonetheless, the current technology for identifying external damage and safe distances for transmission lines is weak, leading to frequent false alarms and low efficiency in practical applications. To address this issue, this paper systematically reviews and summarizes the progress of distance recognition technology in power systems. First, it outlines the spatial distance perception performance requirements for models in transmission line corridor scenarios and summarizes the spatial distance data characteristics of these corridors. Second, the paper categorizes existing common safety distance recognition methods from the perspectives of active and passive ranging, analyzing their advantages and disadvantages in the context of transmission line corridors. Finally, this paper summarizes the types and performance characteristics of current deep learning-based distance recognition methods and proposes a potential application framework for these methods in transmission line corridor scenarios. It also discusses the future application prospects and challenges faced by this framework.