Lane is critical in the vision navigation system of intelligent vehicles. Naturally, the lane is a traffic sign with high-level semantics, whereas it owns the specific local pattern which needs detailed low-level features to localize accurately. Using different feature levels is of great importance for accurate lane detection, but it is still under-explored. On the other hand, current lane detection methods still struggle to detect complex dense lanes, such as Y-shape or fork-shape. In this work, we present Cross Layer Refinement Network aiming at fully utilizing both high-level and low-level features in lane detection. In particular, it first detects lanes with high-level semantic features and then performs refinement based on low-level features. In this way, we can exploit more contextual information to detect lanes while leveraging local-detailed features to improve localization accuracy. We present Fast-ROIGather to gather global context, which further enhances the representation of lane features. To detect dense lanes accurately, we propose Correlation Discrimination Module (CDM) to discriminate the correlation of dense lanes, enabling nearly cost-free high-quality dense lane prediction. In addition to our novel network design, we introduce LineIoU loss which regresses lanes as a whole unit to improve localization accuracy. Experiments demonstrate our approach significantly outperforms the state-of-the-art lane detection methods.

Lane is critical in the vision navigation system of the intelligent vehicle. Naturally, lane is a traffic sign with high-level semantics, whereas it owns the specific local pattern which needs detailed low-level features to localize accurately. Using different feature levels is of great importance for accurate lane detection, but it is still under-explored. In this work, we present Cross Layer Refinement Network (CLRNet) aiming at fully utilizing both high-level and low-level features in lane detection. In particular, it first detects lanes with high-level semantic features then performs refinement based on low-level features. In this way, we can exploit more contextual information to detect lanes while leveraging local detailed lane features to improve localization accuracy. We present ROIGather to gather global context, which further enhances the feature representation of lanes. In addition to our novel network design, we introduce Line IoU loss which regresses the lane line as a whole unit to improve the localization accuracy. Experiments demonstrate that the proposed method greatly outperforms the state-of-the-art lane detection approaches. Code is available at: https://github.com/Turoad/CLRNet.