Research On Several Key Technologies Of Power Line Inspection Based On UAV Machine Vision

With the rapid development of the power industry,the scale of my country’s power grid continues to expand,and transmission lines are spread all over the country.While bringing great convenience to the people of the country,it also brings great challenges to the inspection work of transmission lines.Compared with other industrial sectors,transmission line faults are more dangerous and require higher stability.Therefore,smarter and more efficient inspection methods are worth studying.Aiming at key issues such as automatic identification of insulators,extraction of transmission lines,and detection of foreign objects in transmission lines faced by UAV line inspections,this paper is based on the Convolutional Neural Networks(CNN)model to study related key issues.The main research work and results of this papers are as follows:(1)An insulator identification algorithm based on ICNS(Improved Corner NetSaccade)is proposed.In order to improve the speed and accuracy of insulator recognition,this paper proposes an improved Corner Net-Saccade recognition algorithm.This algorithm starts from the perspective of compressed network structure and improved algorithm,and uses expanded convolution instead of traditional convolution to increase the acquisition of small target features.In the receptive field,the improved residual module is used to realize a multi-layer interactive structure of feature extraction network,so as to improve its detection speed without losing accuracy.Experiments have proved that the detection speed of this algorithm is significantly higher than that of the traditional Corner Net-Saccade algorithm,while ensuring the accuracy and recall rate of recognition.(2)A transmission line extraction algorithm based on Hough one-dimensional transform is studied.Due to the complex and diverse backgrounds of UAV aerial images,some preprocessing needs to be done before extracting power transmission lines.In the denoising process,a denoising method based on wavelet transform is used to preserve more image details;in edge detection,an improved Canny edge detection algorithm with double thresholds is studied to make the edge of the transmission line have better continuity Straight line detection algorithm based on Hough one-dimensional transformation is studied,and more data is retained in line extraction.The experimental results show that the improved algorithm can achieve line extraction more effectively.(3)A foreign body detection algorithm for transmission lines based on FasterRCNN is studied.In order to improve the accuracy of foreign body identification on transmission lines,this paper uses feature fusion and multi-scale prediction methods to improve the RPN network to improve the detection accuracy and efficiency of the network;because of different application scenarios,the method of redesigning the anchor point size is used to improve the volume Jaeger’s network performance.Comparative experiments show that the algorithm in this paper is more accurate than the traditional Faster-RCNN algorithm.At the same time,the algorithm in this paper has a higher degree of overlap with the real transmission line fault when detecting the target,and has a better detection effect.Aiming at several key problems in UAV line inspection,the target detection algorithm proposed in this paper can quickly and accurately identify insulators in UAV aerial images and foreign objects on transmission lines,in order to achieve automatic UAV line inspection and automatic fault detection.Recognition provides a solution,and research has a good prospect for engineering applications.This paper has 42 figures,9 tables and 80 references.