Power line database creating method based on machine learning

During close-range flying tasks, helicopters are frequently struck electrical lines. Helicopter terrain awareness and warning system (HTAWS) is an essential instrument for preventing helicopter crashes, however it is challenging to reliably assure helicopter flight safety in the absence of a power line database. This research suggests a machine learning-based approach for building a power line database using satellite imagery. Aiming at the problem that YOLOv5 is insensitive to small target detection and has a high rate of missed detection, an improved YOLOv5 is proposed to identify the pylons in satellite images. Than the geospatial data abstraction library (GDAL) module was then used to calculate the longitude and latitude of the pylons. A pylons height acquisition method based on the shadow of pylons was proposed. The prediction of pylon connections was investigated using group features of pylons and graph neural networks. The simulation results demonstrate that the approach presented in this research can determine the longitude and latitude of pylons with 94.65% accuracy when determining the tower’s height. The Matthews correlation coefficient (MCC) value predicted for the connection of pylons is 0.479, which can establish a power line database that meets the requirements of helicopter power line collision warning.