用预训练模型做知识引导，提升复杂场景下车道线检测精度。
SAMIRO: Spatial Attention Mutual Information Regularization with a Pre-trained Model as Oracle for Lane Detection
- 引入空间注意力与互信息正则化，利用预训练模型传递上下文知识。
- 在CULane、Tusimple等数据集上显著提升多类模型的检测性能。
- 可即插即用，适合需要鲁棒车道线识别的自动驾驶系统部署。
车道线检测是未来智能交通的关键技术。真实环境中的背景杂乱、光照变化和遮挡等问题，给基于数据驱动的方法带来挑战，尤其在数据采集与标注成本高昂的情况下。为此，车道线检测需充分利用周围车道与物体的上下文及全局信息。本文提出一种基于预训练模型作为知识源的时空注意力互信息正则化方法（SAMIRO）。该方法通过迁移预训练模型的知识，同时保留与领域无关的空间信息，增强检测性能。得益于其即插即用特性，SAMIRO可集成至多种先进检测模型，并在CULane、Tusimple和LLAMAS等多个主流基准上进行充分实验。结果表明，SAMIRO在不同模型与数据集上均实现一致性能提升。代码将在发表后公开。
原文摘要 · Abstract (English)
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 pretrained 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.
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