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语义分割是自主车辆理解周围场景的关键技术。对于实际的自主车辆，不希望花费大量的推理时间来获得高精度的分割结果。使用轻量级架构(编码器解码器或双通道)或对低分辨率图像进行推理，最近的方法实现了非常快速的场景解析，甚至可以在单个1080Ti GPU上以100 FPS以上的速度运行。然而，在这些实时方法和基于膨胀主干的模型之间仍然存在明显的性能差距。 为了解决这个问题，受HRNet的启发，作者提出了一种具有深度高分辨率表示能力的深度双分辨率网络，用于高分辨率图像的实时语义分割，特别是道路行驶图像。作者提出了

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