SCINet: Semantic Cue Infusion Network for Lane Detection

Nowadays, lane detection plays an important role in autonomous driving. However, the task of lane detection still faces many challenges, such as external no-visual-clue and internal sparse supervisory signals. In this work, we propose a novel Semantic Cue Infusion Network (SCINet) that uses semantic cues to aid lane detection. Specifically, in order to overcome the external no-visual-clue condition, we introduce a strategy that utilizes semantic cues as additional supervisory signals, which facilitate SCINet to collect region-aware features in the shared layer. We also design a hypernetwork with the additional signals used as a critical part to generate dynamic weights for downstream output heads. Furthermore, we design two Slice Attention Modules (SAMs) based on the interdependencies between slices to improve the robustness of SCINet in distinguishing features between lanes and background. Experiments on two popular lane detection benchmarks (i.e., TuSimple and CULane) show that SCINet significantly outperforms several state-of-the-art methods.