Key Point Detection of Power Tower Based on Improved Yolov5

Using UAV to accurately and quickly realize the three-dimensional modeling of power tower is an effective measure to promote the construction of smart grid. A tower target recognition algorithm based on yolov5 is presented to solve the problem of inaccurate tower 3D perception recognition by UAV. By improving the internal structure of the residual block to reduce the impact of steel intersection on the identification results and improve the accuracy of the algorithm, the exponential linear unit function is used as the activation function to accelerate the convergence speed of the network and improve the robustness of the algorithm, so as to realize the rapid and accurate identification of key points of power towers. By comparing the fast R-CNN and EfficientDet target detection methods through experiments, the improved algorithm has improved the recognition accuracy to a certain extent. The model maintains the lightweight characteristics of yolov5 and has a good prospect in application deployment.