Papers › Efficient Road Lane Marking Detection with Deep Learning
Efficient Road Lane Marking Detection with Deep Learning
Ping-Rong Chen, Shao-Yuan Lo, Hsueh-Ming Hang, Sheng-Wei Chan, Jing-Jhih Lin
Lane mark detection is an important element in the road scene analysis for Advanced Driver Assistant System (ADAS). Limited by the onboard computing power, it is still a challenge to reduce system complexity and maintain high accuracy at the same time. In this paper, we propose a Lane Marking Detector (LMD) using a deep convolutional neural network to extract robust lane marking features. To improve its performance with a target of lower complexity, the dilated convolution is adopted. A shallower and thinner structure is designed to decrease the computational cost. Moreover, we also design post-processing algorithms to construct 3rd-order polynomial models to fit into the curved lanes. Our system shows promising results on the captured road scenes.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Real-Time Semantic Segmentation | CamVid | LMDNet | Frame (fps) | 34.4 (1080) | #26 of 29 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | CamVid | LMDNet | Time (ms) | 29.1 | #26 of 29 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | CamVid | LMDNet | mIoU | 63.5 | #26 of 29 | Archive leaderboard | report |
| Semantic Segmentation | CamVid | LMDNet | Mean IoU | 63.5 | #17 of 21 | 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.
Methods
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