Automatic recognition and positioning of overpass based on Faster R-CNN

doi:10.13474/j.cnki.11-2246.2021.0073

Abstract

Abstract: The automatic recognition of overpass structures is of great significance for multi-scale modeling, spatial analysis and vehicle navigation of road network. The traditional method of overpasses recognition based on vector data relies too much on the characteristics of manual design and has poor adaptability to complex scenes. In this paper, a method for overpasses identification based on the target detection model Faster R-CNN is proposed. This method uses convolutional neural network to learn the deep structural characteristics of samples, and then realizes the automatic recognition and accurate positioning of the overpasses. The experimental results show that the method has a good recognition effect on overpasses, and can accurately determine their positions in the complex road network, avoiding the influence of human intervention on the uncertainty of results, and has a strong anti-interference.

Key words: overpass, target detection, Faster R-CNN, deep neural network, pattern recognition

CLC Number:

P237

MA Jingzhen, CHEN Huanxin, ZHU Xinming, ZHANG Fubing. Automatic recognition and positioning of overpass based on Faster R-CNN[J]. Bulletin of Surveying and Mapping, 2021, 0(3): 28-32,86.

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