RNGDet: Road Network Graph Detection by Transformer in Aerial Images
Zhenhua Xu, Yuxuan Liu, Lu Gan, Yuxiang Sun, Xinyu Wu, Ming Liu and, Lujia Wang

TL;DR
RNGDet introduces a transformer-based method with imitation learning to automatically detect accurate road network graphs from aerial images, improving topology correctness and detection precision for autonomous vehicle applications.
Contribution
The paper presents a novel transformer and imitation learning approach for iterative, vertex-by-vertex road network graph detection from aerial images, addressing limitations of previous segmentation and graph-based methods.
Findings
Outperforms existing methods in topology correctness and detection accuracy
Handles complex intersections with various incident road segments
Validated on a public dataset with superior experimental results
Abstract
Road network graphs provide critical information for autonomous-vehicle applications, such as drivable areas that can be used for motion planning algorithms. To find road network graphs, manually annotation is usually inefficient and labor-intensive. Automatically detecting road network graphs could alleviate this issue, but existing works still have some limitations. For example, segmentation-based approaches could not ensure satisfactory topology correctness, and graph-based approaches could not present precise enough detection results. To provide a solution to these problems, we propose a novel approach based on transformer and imitation learning in this paper. In view of that high-resolution aerial images could be easily accessed all over the world nowadays, we make use of aerial images in our approach. Taken as input an aerial image, our approach iteratively generates road network…
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