Risk Identification Method of Overhead Line Tension Setting out Construction Based on Stacking Depth Convolution

During the tension construction of UHV overhead transmission lines, it is very important for power departments to carry out construction risk monitoring and identification to better control the construction of tension machines, traction ropes and guidance. Therefore, this study proposes an overhead line tension setting out construction risk identification method based on stacking depth convolution. First, the UAV is equipped with acquisition equipment to collect the construction image of overhead line tension setting out, and the image is denoised and histogram equalization. Taking the processed construction image as the input information, the identification model based on the stacking depth convolution neural network is used to identify the construction risk of force setting out. The experimental results show that the F-measure values of the risk identification results of this method are more than 0.9, which shows that this method is effective.