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The RGB-Thermal (RGB-T) information for semantic segmentation has beenextensively explored in recent years. However, most existing RGB-T semanticsegmentation usually compromises spatial resolution to achieve real-timeinference speed, which leads to poor performance. To better extract detailspatial information, we propose a two-stage Feature-Enhanced Attention Network(FEANet) for the RGB-T semantic segmentation task. Specifically, we introduce aFeature-Enhanced Attention Module (FEAM) to excavate and enhance multi-levelfeatures from both the channel and spatial views. Benefited from the proposedFEAM module, our FEANet can preserve the spatial information and shift moreattention to high-resolution features from the fused RGB-T images. Extensiveexperiments on the urban scene dataset demonstrate that our FEANet outperformsother state-of-the-art (SOTA) RGB-T methods in terms of objective metrics andsubjective visual comparison (+2.6% in global mAcc and +0.8% in global mIoU).For the 480 x 640 RGB-T test images, our FEANet can run with a real-time speedon an NVIDIA GeForce RTX 2080 Ti card.

FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation