  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.
