Defect Recognition Of Key Components Of Transmission Line Based On Convolutional Neural Network

With the expansion,complexity and geographical span of China’s power grid,the safety and reliable operation of power grid has been widely concerned.As one of the important links of power system,transmission lines are exposed in the field for a long time during operation.Its components are prone to rust,damage,broken strands,foreign bodies and other defects.This brings great hidden dangers to the safe operation of transmission lines.Transmission line use environment includes complex terrain,inconvenient traffic conditions and harsh climate environment.The traditional manual inspection of transmission lines has many disadvantages,such as high labor intensity,low efficiency,low security of inspection personnel and limited inspection results by personnel skills.It is difficult to adapt to the development and safe operation of modern power grid.In recent years,with the development of unmanned air vehicle(UAV)technology and UAV assisted line inspection system,UAV assisted line inspection system with low cost,high efficiency and adaptability to complex environment has completely replaced the traditional inspection method.Through timely processing of UAV inspection images,the basic status of transmission lines can be obtained and equipment defects and hidden troubles can be found.However,the rapid growth of the power grid and large number of applications of helicopters and unmanned aerial patrol lines have resulted in a dramatic increase in the number of aerial images.The contradiction between the image detection of key components of transmission lines and the number of inspection personnel has become increasingly prominent.Therefore,combining UAV inspection data and deep learning technology is helpful to improve the timeliness and automation degree of transmission line defect inspection.This is great significant to ensure the safe operation of the power system.In this study,a sample detection dataset of transmission lines is constructed by using UAV inspection images;the efficiency and insufficiency of SSD algorithm and Faster-RCNN algorithm for multi-target defect recognition of transmission lines are compared and analyzed;and an optimization model for multi-objective defect recognition of transmission lines is established based on an improved Faster-RCNN.The main research contents are as follows:(1)By augmenting and labeling the UAV inspection images,a sample detection dataset of transmission lines is made for the experiment by using the UAV inspection image provided by a power grid company in Gansu Province,which contains seven categories of targets:normal_ring,error_ring,not_rush,is_rush,have_shim,no_shim and nest.At the same time,the dataset was preprocessed by the contrast enhancement,denoising and equalization using linear transformation,mean filter and enhancement algorithm based on the HSI model,respectively.Finally,the annotated and augmented dataset contains 8400 images.(2)Using the self-built transmission lines dataset,under the VGG16 backbone feature extraction network,experimental results show that the effectiveness of the Faster-RCNN algorithm for multi-target defect identification of transmission lines(average accuracy rate of 86.57%)is significantly higher than that of SSD algorithm(average accuracy rate of 57.16%).However,the accuracy of Faster-RCNN algorithm for multi-target defect recognition under complex surface background is significantly lower than that of simple surface background.(3)To improved the Faster-RCNN algorithm,adopted VGG16 as the feature extraction network and selected alternating optimization training method,and the ROI(Region of Interest)Pooling in convolutional neural network was improved to ROI Align regional feature aggregation.Results indicate that the improved Faster-RCNN for target recognition on transmission lines had the mean average Precision(mAP)was 92.47%and the average detection time was 0.307s,which can meet the multi-target defect identification and detection requirements under the background of complex underlying surfaces of transmission lines.Capitalizing on these study findings,this study proposes a multi-objective defect recognition optimization model for transmission lines under natural conditions based on improved Faster-RCNN.The model has good accuracy,real-time and robustness,and can provide intelligent and effective decision-making basis for the identification of transmission lines defects in actual line inspections.