Research And Implementation Of Key Technologies Of Quad-rotor UAV Power Line Inspection Based On Machine Vision

With the rapid development of UAV technology,the stability,endurance and load capacity of UAVs have been greatly improved.So,the use of UAVs for power inspection has become one of the research hotspots.Aiming at the autonomous navigation and insulator fault detection of quadrotor UAVs during the power line inspection,this paper studies vision-based obstacle detection and avoidance,autonomous landing,and methods to improve the accuracy of insulator string failures.In terms of UAV obstacle avoidance,GPS or Beidou navigation is used to control the UAV’s autonomous flight,and the obstacles on the flight path are detected by binocular cameras.The disparity map is generated through the matched feature point of obstacles,so as to obtain the distance between the UAV and the obstacle.Taking the edge detection point closest to the obstacle by the UAV as the center,construct a collision circle that surrounds the obstacle and reserves a safe distance on the horizontal plane where the UAV is flying or the vertical plane parallel to the flying direction.This flight strategy controls the UAV to fly along the outer tangent line from itself to the collision circle to realize the obstacles avoidance of the UAV.In terms of autonomous landing of UAVs,this paper uses binocular cameras to reconstruct the three-dimensional point cloud information on the ground.According to the depth and the angle between the UAV and the ground,a relatively flat plane is first fitted and converted into two-dimensional information.Secondly,learn features according to the characteristics of the landable ground and obtain the landable plane characteristics.Finally,input the twodimensional information into the trained random forest classifier to determine whether it is a landable plane.Experiments prove that the UAV can judge the landability of the ground at different heights and land stably.This proves that the method can effectively judge the landing ground and realize the autonomous landing function of the UAV.In terms of improving the accuracy of insulator string drop failures,this paper uses the Retina Net with adaptive training sample selection and generalized focus loss function to strengthen effective positive sample selection.Then use the balanced feature pyramid to enhance the feature extraction.The more accurate sample selection algorithm and the balanced pyramid feature pyramid are combined into the Retina Net.Experiments show that the improved Retina Net can effectively improve the detection accuracy of the insulator drop failures.In this paper,through the research on the autonomous navigation technology of UAV and the insulator fault detection technology in the inspection process,the UAV is used to realize the automatic power line inspection,which reduces the work risk of power line inspection and improves work efficiency.