Target Detection And Physical Localization Of Power Poles And Towers In Inspection Images Taken By Uav Based On Deep Learning

Typhoons are frequent and destructive in the southern coastal areas of China,greatly affecting the safety and stability of the power grid.When the power grid is hit by typhoons,mudslides and other disasters,the towers of transmission and distribution lines are prone to tilting and collapsing,which can easily lead to line failures and consequently power outages.To quickly and accurately locate abnormal towers and restore power supply promptly,power operation and maintenance departments usually use unmanned aerial vehicles(UAVs)for post-disaster power tower inspections.However,the massive inspection images generated by UAV inspections are still mainly processed manually,which is not only time-consuming and laborious but also prone to omissions and misjudgments.Therefore,this dissertation adopts deep learning-based image recognition technology for intelligent detection of power poles and towers and uses the metadata of UAV inspection images to calculate the GPS information of power poles and towers through coordinate transformation,to provide auxiliary decision-making information for rapid repair of power poles and towers after disasters and improve line repair capability.The main research content of this dissertation is as follows.Firstly,the image enhancement technology applicable to UAV power pole and tower inspection images is studied,and a database of UAV power pole and tower inspection images of uniform category and moderate quantity is constructed.To improve the robustness of the model for image recognition under various environments,image enhancement methods such as distortion and filtering are used to extend the image samples according to the principle of VRM(Vicinal Risk Minimization).To solve the problem of the small size of power poles and towers in the whole inspection image,the number of small target towers in the data set is increased by using mosaic enhancement.Secondly,a deep learning-based power pole and tower detection model for UAV inspection images was designed.The model is based on the YOLOv3 algorithm and the improvements to the prior frame and network structure based on the original algorithm is proposed.The effectiveness of the algorithm improvements is verified through experiments and the performance of the improved YOLOv3 algorithm is compared with that of the YOLOv4 algorithm.Finally,in order to apply the algorithm model in embedded devices with limited computational and storage resources,this dissertation also uses channel pruning techniques to compress the trained pole tower detection model,reducing the model parameters by more than 90% with a small loss of accuracy,which further improves the detection speed.Thirdly,a power pole and tower physical localization algorithm based on UAV inspection images is studied.The algorithm firstly uses coordinate transformation based on the metadata of the image to unify the UAV,pixel point of the power pole or tower,and the actual physical position of the power pole or tower into the spatial rectangular coordinate system,then solves the collinear equation to obtain the coordinate of the power pole or tower in the spatial rectangular coordinate system,and finally transforms the coordinate to the geodetic coordinate system to obtain the GPS coordinates of the power pole or tower.In this dissertation,the method is evaluated by a case study and the experiments show that the error is within a reasonable range.Besides,considering the existence of random errors caused by sensor measurements in the UAV metadata,this dissertation also uses Monte Carlo methods to verify that the calculated GPS errors obtained for the pole towers remain within a reasonable range in the presence of errors in the metadata,further demonstrating the effectiveness of the method.Finally,a software system for the image recognition and disaster damage information statistics of UAV power pole and tower inspection was designed and developed based on the Python/Django framework.The algorithm modules and user interfaces for image data management,image data processing,and disaster damage information statistics were designed respectively to achieve end-to-end target detection and location of UAV power pole and tower inspection images after disasters.