SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving
SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving
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DOI：
10.1109/icra46639.2022.9812134
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发表时间：
2021-09
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影响因子：
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通讯作者：
W. G. C. Bandara;Jeya Maria Jose Valanarasu;Vishal M. Patel
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作者：
W. G. C. Bandara;Jeya Maria Jose Valanarasu;Vishal M. Patel
道路提取是构建自主导航系统的重要步骤。检测路段是具有挑战性的，因为它们的宽度不同，在整个图像中分叉，并且经常被地形、云或其他天气条件遮挡。仅使用卷积神经网络（ConvNets）来解决这个问题是无效的，因为它无法捕获图像中道路段之间的距离依赖关系，而这对于提取道路连接至关重要。为此，我们提出了一个空间和交互空间图推理（SPIN）模块，当插入卷积神经网络时，该模块对从特征映射投影的空间和交互空间上构建的图进行推理。空间空间推理提取不同空间区域之间的依赖关系和其他上下文信息。在投影的交互空间上进行推理有助于从图像中存在的其他地形中适当地描绘道路。因此，SPIN提取道路段之间的远程依赖关系，并有效地从其他语义中描绘道路。我们还引入了一个自旋金字塔，它在多个尺度上进行自旋图推理，以提取多尺度特征。我们提出了一种基于堆叠沙漏模块和自旋金字塔的道路分割网络，与现有方法相比，该网络具有更好的性能。此外，我们的方法计算效率高，在训练过程中显著提高了收敛速度，使其适用于大规模高分辨率航空图像。代码可在：https://github.com/wgcban/SPIN_RoadMapper.git。
Road extraction is an essential step in building autonomous navigation systems. Detecting road segments is challenging as they are of varying widths, bifurcated throughout the image, and are often occluded by terrain, cloud, or other weather conditions. Using just convolution neural networks (ConvNets) for this problem is not effective as it is inefficient at capturing distant dependencies between road segments in the image which is essential to extract road connectivity. To this end, we propose a Spatial and Interaction Space Graph Reasoning (SPIN) module which when plugged into a ConvNet performs reasoning over graphs constructed on spatial and interaction spaces projected from the feature maps. Reasoning over spatial space extracts dependencies between different spatial regions and other contextual information. Reasoning over a projected interaction space helps in appropriate delineation of roads from other topographies present in the image. Thus, SPIN extracts long-range dependencies between road segments and effectively delineates roads from other semantics. We also introduce a SPIN pyramid which performs SPIN graph reasoning across multiple scales to extract multi-scale features. We propose a network based on stacked hourglass modules and SPIN pyramid for road segmentation which achieves better performance compared to existing methods. Moreover, our method is computationally efficient and significantly boosts the convergence speed during training, making it feasible for applying on large-scale high-resolution aerial images. Code available at: https://github.com/wgcban/SPIN_RoadMapper.git.