SPNet: An RGB-D Sequence Progressive Network for Road Semantic Segmentation

Road semantic segmentation is an essential component of autonomous driving and blind navigation. Although many excellent RGB-based road semantic segmentation algorithms have been proposed, these methods may not detect correctly due to the lack of geometric information. Recently, RGB-D road semantic segmentation methods attract more research attention. However, the existing RGB-D methods ignore the impact of unknown noise in sensors. To solve this problem, we propose an RGB-D Sequence Progressive Network (SPNet) for road semantic segmentation. Specifically, we first propose a sequence-based RGB-D feature extractor to alleviate the effect of noise. Then, We propose a multi-modal feature fusion (MMFF) module to enhance the feature representation of multi-modal data by further alleviating the effect of noise. Finally, we propose a semantic flow prediction (SFP) module that aims to align the multi-modal features in the decoder. Extensive experiments are conducted on several challenging datasets, including KITTI and GMRP. Our method achieves an F-score of 97.21% on the KITTI official leaderboard and ranked third in the official leaderboard.