Papers › CLRNet: Cross Layer Refinement Network for Lane Detection

CLRNet: Cross Layer Refinement Network for Lane Detection

19 Mar 2022CVPR 2022 1arXiv:2203.10350archive 2025-07-28

Tu Zheng, Yifei HUANG, Yang Liu, Wenjian Tang, Zheng Yang, Deng Cai, Xiaofei He

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.

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Tasks

Lane Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Lane Detection CULane CLRNet(DLA-34) F1 score 80.47 #9 of 63 Archive leaderboard report
Lane Detection CULane CLRNet(ResNet-101) F1 score 80.13 #14 of 63 Archive leaderboard report
Lane Detection CULane CLRNet(ResNet-34) F1 score 79.73 #18 of 63 Archive leaderboard report
Lane Detection CULane CLRNet(ResNet-18) F1 score 79.58 #22 of 63 Archive leaderboard report
Lane Detection LLAMAS CLRNet (DLA-34) F1 0.9612 #1 of 10 Archive leaderboard report
Lane Detection LLAMAS CLRNet (ResNet-18) F1 0.9600 #4 of 10 Archive leaderboard report
Lane Detection TuSimple CLRNet(ResNet-34) Accuracy 96.9% #5 of 43 Archive leaderboard report
Lane Detection TuSimple CLRNet(ResNet-34) F1 score 97.82 #5 of 43 Archive leaderboard report
Lane Detection TuSimple CLRNet(ResNet-18) Accuracy 96.82% #7 of 43 Archive leaderboard report
Lane Detection TuSimple CLRNet(ResNet-18) F1 score 97.89 #7 of 43 Archive leaderboard report
Lane Detection TuSimple CLRNet(ResNet-101) F1 score 97.62 #41 of 43 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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