Recently, the task of lane detection has been greatly improved with the rapid development of deep learning and autonomous driving. However, there exist limitations like the challenging complex scenarios and real-time ef-ficiency. In this paper, we present a novel Attention Based Dual Path Network (ADPNet) to handle the task of lane detection. The ADPNet treat the process of lane detection as a task of binary semantic segmentation, where the Detail Path is designed to capture detailed low-level information and the Semantic Path with dual attention module is designed to capture contextual high-level information. We use the Feature Aggregation Module to fuse the information of the two paths, followed by the process of lane fitting to get a parametric description of lanes. The proposed ADPNet achieves good trade-off between the accuracy and real-time efficiency on TuSimple and CULane, which are two popular lane detection benchmark datasets. The results demonstrate that our architecture outperforms the current state-of-the-art methods.
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