SAMIRO: Spatial Attention Mutual Information Regularization with a Pre-trained Model as Oracle for Lane Detection
Hyunjong Lee, Jangho Lee, Jaekoo Lee
车道检测是未来移动解决方案中的一个重要话题。 背景杂乱,不同的照明和遮挡等现实世界的环境挑战对有效车道检测构成了重大障碍,特别是当依靠数据驱动的方法时,需要大量精力和数据收集和注释的成本。 为了解决这些问题,车道检测方法必须利用来自周围车道和物体的上下文和全球信息。 在本文中,我们提出了一个空间注意力相互信息规范化,其预训练模型为Oracle,称为SAMIRO。 SAMIRO通过从预训练模型传输知识,同时保留与域无关的空间信息,从而增强车道检测性能。 利用SAMIRO的即插即用特性,我们将它集成到各种最先进的车道检测方法中,并在CULane,Tusimple和LLAMAS等主要基准上进行广泛的实验。 结果表明,SAMIRO持续提高不同模型和数据集的性能。 该代码将在发布时提供。
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 pap...