Research On Visual Detection Method Of High-Voltage Line Inspection Robot

500 kV high-voltage line is the major long-distance transmission system of our country,which plays an extremely important role in the national construction process.With the development of transmission line inspection robot technology and smart grid,more and more attention has been paid to automatic detection of high voltage fault.The traditional autonomous detection system mainly relies on the sensors such as ultrasonic and eddy current carried by the robot to complete the task of recognizing obstacle and detecting line fault,which has the defects of low efficiency and poor timeliness.With the development of computer vision,scholars at home and abroad gradually integrate it into the inspection robot automatic detection system,which improves the intelligent level of inspection robot.At present,there are many problems such as poor adaptability and low accuracy in vision-based inspection line robot monitoring system.In order to solve above problems,this thesis develops a prototype robot and presents a visual obstacle avoidance scheme and presents a visual detection method for transmission line faults.Aiming at the performance requirements of high-voltage intelligent inspection,a robot prototype is designed and a visual obstacle avoidance scheme is proposed.According to the characteristics of 500 kV transmission lines and the basic functions of walking and obstacle avoidance,a three-arm suspension inspection line robot system is designed.The robot uses the circular guide rail as the driving support arm,which has the characteristics of novel design,simple control,stable motion and so on.Inspection line robot in the course of movement must identify hammer and other obstacles,and according to their type planning obstacle behavior.Based on the characteristics of obstacle,the obstacle identification is carried out.The obstacle is identified by Haar feature and HOG feature training obstacle classifier.The Haar feature and HOG feature are used to train the obstacle classifier,and then the classifier and structure constraint are used to determine the obstacle type.According to the texture features of the wires,the texture features of the wires are enhanced by the combination of the gray level co-occurrence matrix and the clustering algorithm,and the high-voltage lines are segmented.Using the HSV color and contour feature of driving wheel image,the recognition of driving wheel can be realized,and the self-aligning operation of inspection robot can be realized.The experimental results show that the scheme can identify the obstacle type accurately and guide the robot to overcome the obstacles.Aiming at the task of intelligent inspection of high voltage line,a visual detection scheme of transmission line fault is put forward.In this thesis,the salient features of high-voltage fault image are analyzed,and the appropriate saliency model is used to process the image and obtain the saliency map.Then the high voltage line is segmented from the image by FT segmentation method.And the segmentation image is further processed according to the fault characteristic of the transmission line.Then the fault type is detected and its position is determined.The feasibility of the method is verified by processing the fault image.