SAMIRO: Spatial Attention Mutual Information Regularization with a pre-trained model as Oracle for lane detection

Lane detection is an important topic in the future mobility solutions. Real-world environmental challenges such as background clutter, varying illumination, and occlusions pose significant obstacles to effective lane detection, particularly when relying on data-driven approaches that require substantial effort and cost for data collection and annotation. To address these issues, lane detection methods must leverage contextual and global information from surrounding lanes and objects. In this paper, we propose a Spatial Attention Mutual Information Regularization with a pre-trained model as an Oracle , called SAMIRO . SAMIRO enhances lane detection performance by transferring knowledge from a pre-trained model while preserving domain-agnostic spatial information. Leveraging SAMIRO’s plug-and-play characteristic, we integrate it into various state-of-the-art lane detection approaches and conduct extensive experiments on major benchmarks such as CULane, Tusimple, and LLAMAS. The results demonstrate that SAMIRO consistently improves performance across different models and datasets. The code will be made available upon publication. • We propose SAMIRO, a novel regularization framework that improves generalization in lane detection by transferring domain-agnostic knowledge from a pre-trained model. • SAMIRO is plug-and-play and scalable with state-of-the-art lane detection methods. • We validate SAMIRO on public benchmarks (CULane, Tusimple, and LLAMAS), showing that it achieves performance comparable to or surpassing SOTA models.