Modern deep-learning-based lane detection methods are successful in most scenarios but struggling for lane lines with complex topologies. In this work, we propose CondLaneNet, a novel top-to-down lane detection framework that detects the lane instances first and then dynamically predicts the line shape for each instance. Aiming to resolve lane instance-level discrimination problem, we introduce a conditional lane detection strategy based on conditional convolution and row-wise formulation. Further, we design the Recurrent Instance Module(RIM) to overcome the problem of detecting lane lines with complex topologies such as dense lines and fork lines. Benefit from the end-to-end pipeline which requires little post-process, our method has real-time efficiency. We extensively evaluate our method on three benchmarks of lane detection. Results show that our method achieves state-of-the-art performance on all three benchmark datasets. Moreover, our method has the coexistence of accuracy and efficiency, e.g. a 78.14 F1 score and 220 FPS on CULane. Our code is available at https://github.com/aliyun/conditional-lane-detection.

翻译：在多数情况下,现代的基于深层次学习的车道探测方法都是成功的,但是在复杂的地形下,我们建议CondLaneNet,这是一个创新的自上而下车道探测框架,首先探测车道情况,然后动态地预测每一车道的形状。为了解决车道实例一级的歧视问题,我们采用有条件的基于有条件变迁和行式制式的有条件的车道探测战略。此外,我们还设计了经常程序模块(RIM),以克服用密度线和叉线等复杂地形探测车道的问题。从需要很少后处理的端到端管道获益,我们的方法具有实时效率。我们广泛评价了我们关于三条车道探测基准的方法。结果显示,我们的方法在所有三个基准数据集中都达到了最新业绩。此外,我们的方法具有准确性和效率共存,例如CulLane的78.14 F1分和220 FPS。我们的代码可以在 https://github.com/aliyun/stimal-lane-dection上查到。