Research On Transmission Line Component Detection Algorithm Based On Power Vision

Transmission lines play an important role in the power grid,and their faults will not only affect our normal daily electricity consumption,but also bring a lot of economic losses.Therefore,detecting the safe operation of power lines and insulators in transmission lines is important for the safe and stable operation of the power grid.one ring.Since most of the traditional power line inspection methods are manual inspections,it takes a lot of manpower and material resources,it is difficult to ensure the personal safety of the staff,and the inspection efficiency is also very low.In order to solve the problem of low efficiency and labor in traditional manual inspection,the text detects and recognizes power line images and insulator images in unmanned inspection images respectively.Based on the analysis of the characteristics of unmanned inspection images,experiments are carried out Result analysis.The main research work of this paper is as follows:(1)A fast power line detection algorithm based on improved Ratio algorithm and Hough transform is proposed.In this paper,by analyzing the power line images collected by UAV inspection,a fast power line detection algorithm based on improved Ratio algorithm and Hough transform is proposed.On the basis of image enhancement processing of the inspection image,the improved Ratio algorithm is used to detect the edge of the power line combined with the denoising method,and the edge extraction of the power line is realized by combining the Hough transform and the straight-line grouping connection algorithm,which can eliminate the background near the power line in the image.The interference of noise,the complete extraction of power lines in the interference of many complex background information,provides high technical support for the intelligent inspection method of UAV.(2)A transmission line insulator detection algorithm based on improved Faster RCNN is proposed.Aiming at the problem that the inspection insulator image data set is insufficient and cannot meet the training set requirements of deep learning,this paper adopts the traditional image expansion method combined with the improved generative adversarial network to expand the image data set,so that the image presents a variety of different transformations.,to augment the insulator image dataset.In order to improve the detection performance of insulators in transmission lines,this paper detects insulators in images by improving the Faster RCNN algorithm.Based on the Faster RCNN algorithm,fine-tuning the ratio of RPN candidate regions,increasing the number of anchors and multi-scale training,etc.Make the detection accuracy of the improved Faster RCNN network higher and improve the detection time of the algorithm.