A Recognition Model for Transmission Line Pole License Plates Based on Improved Densenet
In response to the difficulties in searching for transmission line pole license plates due to tree obstruction and inadequate drone control technology by some operation and maintenance personnel,resulting in poor quality and efficiency of refined drone inspections,this paper proposes an improved Densenet based recognition model for transmission line pole license plates.The model first adopts a template matching algorithm to locate the position of pole plates in aerial images,replacing traditional global traversal recognition with regional recognition mode,thereby reducing background interference and improving recognition efficiency;Then,this model adopts the methods of feature pre setting to eliminate interference and feature pre setting to identify targets,and improves the dense block of the Densenet model.The improved Densenet model analyzes the area where the pole number plate is located,identifies the pole number plate recognition standards and extracts information,in order to facilitate efficient maintenance of pole number plate information by patrol personnel.The experimental results show that the model proposed in this paper has significantly improved the accuracy of recognizing pole and license plate text and numbers compared to Yolo v7,Crnn,and Densenet models in open areas,tree cover,and other situations.
pole number platetemplate matchingfeature presetDensenet
万方数据
维普
