A novel deep-learning-based method for semantic segmentation of RGB and thermal images is introduced. The proposed method employs a novel neural network design for multi-modal fusion based on multi-resolution patch processing. A novel decoder module is introduced to fuse the RGB and thermal features extracted by separate encoder streams. Experimental results on synthetic and real-world data demonstrate the efficiency of the proposed method compared with state-of-the-art methods.

中文翻译：

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用于语义分割的具有多尺度 RGB-T 融合的多模态神经网络

介绍了一种新的基于深度学习的 RGB 和热图像语义分割方法。所提出的方法采用一种新颖的神经网络设计，用于基于多分辨率补丁处理的多模态融合。引入了一种新颖的解码器模块来融合由单独的编码器流提取的 RGB 和热特征。与最先进的方法相比，合成和真实世界数据的实验结果证明了所提出的方法的效率。