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Automatic identification method of overpasses based on deep learning

The automatic identification of overpass structures is of great significance for multi-scale modeling, spatial analysis, and vehicle navigation of road networks. The traditional method of overpass recognition based on vector data relies too heavily on the characteristics of manual design and has poor adaptability to complex scenes. In this paper, a method for overpass identification based on the target detection model Faster R-CNN (Regions with Convolutional Neural Network) is proposed. This method uses a Convolutional Neural Network to learn the deep structural characteristics of data samples, and then automatically identifies and finds accurate positioning of the overpasses. The experimental results show that this method is able to identify overpasses and can accurately determine their positions in a complex road network, avoiding the influence of human intervention on the uncertainty of results. This method also has strong anti-interference abilities