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：现代基于深度学习的车道检测方法在大多数场景下表现成功，但在处理具有复杂拓扑结构的车道线时仍面临挑战。本文提出CondLaneNet，一种新颖的从顶到底车道检测框架，该框架首先检测车道实例，随后为每个实例动态预测线形。为解决车道实例级判别问题，我们引入基于条件卷积和逐行公式的条件化车道检测策略。此外，我们设计了循环实例模块（RIM）以克服检测密集线、分叉线等复杂拓扑结构车道线的难题。得益于端到端流水线且几乎无需后处理，我们的方法具有实时效率。我们在三个车道检测基准数据集上进行了广泛评估。结果表明，我们的方法在所有三个基准数据集上均达到最优性能。此外，我们的方法兼具准确性与效率，例如在CULane上取得了78.14的F1分数和220 FPS。代码开源于https://github.com/aliyun/conditional-lane-detection。