Research on Detection Algorithm of Utility Pole Signboard Based on Faster R-CNN

As the only sign to record the basic parameter information of transmission line, utility pole signboard plays an important role in the process of circuit statistics, management and inspection, and has a very high research value. At present, the data acquisition of utility pole signboard mainly depends on manual work. Due to the influence of environment, equipment and other factors, the angle of utility pole signboard in the image will be inclined, which will reduce the detection accuracy of utility pole signboard. Through literature research, it is found that there are few academic researches on the detection and identification of such utility pole signboards, while there are many researches and practical solutions in license plate detection and recognition. This paper proposes a new detection algorithm based on Faster R-CNN model and ResNet-50 convolutional neural network, which improves the accuracy by increasing its depth to 50 layers, and improves the generalization ability of the algorithm combined with multi-scale training to achieve the global optimal detection effects. The experimental results show that the proposed algorithm has good detection effects, and the detection accuracy is 87.6%, which verifies the feasibility of pole signboard detection.