Research On Visual Detection And Ranging Of Obstacles In High Voltage Transmission Lines

High-voltage transmission lines are important carriers for transmitting electricity.Due to the long-term exposure of high-voltage transmission lines to the natural environment,constantly experiencing damage from wind,frost,rain and snow and other natural disasters,over time there will be problems such as high-voltage transmission line breakage,corrosion,wear,and hardware damage,if not found and repaired in time,it will bring a lot of inconvenience to production and life,causing huge economic and social losses.However,the traditional manual inspection is labor-intensive,the working conditions are relatively difficult,and the labor efficiency is very low.Drone inspections will have the problem of repeated or missed photos.The popularization of intelligent robots provides a new idea for the inspection of high-voltage transmission lines.If a robot can be designed to walk on a high-voltage transmission line,and a camera device is installed at the front end of the robot,the staff can take pictures through the camera device to conduct a close inspection of the situation on the line,then all the problems mentioned earlier can be solved.If the high-voltage line patrol robot wants to realize autonomous inspection along the wire,it should have the ability to cross obstacles such as insulator strings and dampers on the high-voltage transmission line.Therefore,accurate detection and identification of obstacles is the key technology for the autonomous operation of high-voltage line patrol robots.Aiming at the structural characteristics of 220 k V high-voltage transmission lines,this thesis proposes an algorithm for visual detection and recognition of obstacles in highvoltage transmission lines based on YOLOv5.Introducing CBAM attention mechanism in YOLOv5 algorithm enables the network to adaptively select channel and spatial features of convolutional kernels,improving the accuracy and robustness of the model;The CIOU loss function in YOLOv5 algorithm is replaced by the EIOU loss function,which solves the problem that the CIOU loss function has limitations in the horizontal to vertical ratio in the bounding box regression loss.The m AP value of the improved obstacle detection algorithm is increased by 1.89%,the Precision value is increased by 0.78%,and the Recall value is increased by 3.35%.The final result shows that the improved YOLOv5 model has higher overall performance.The real-time distance detection of obstacles on the line by line patrol robots is also of great significance for the complete automation of line patrol robots.This article uses the principle of monocular ranging to measure the distance of obstacle targets,which facilitates the line patrol robot to cross obstacles in a timely manner and continuously check the route when approaching obstacles.The experimental results indicate that the ranging method can be applied to obstacle crossing planning of line patrol robots.