Lane Mark Detection with Pre-Aligned Spatial-Temporal Attention

Lane mark detection plays an important role in autonomous driving under structural environments. Many deep learning-based lane mark detection methods have been put forward in recent years. However, most of current methods limit their solutions within one single image and do not make use of the de facto successive image input during the driving scene, which may lead to inferior performance in some challenging scenarios such as occlusion, shadows, and lane mark degradation. To address the issue, we propose a novel lane mark detection network which takes pre-aligned multiple successive frames as inputs to produce more stable predictions. A Spatial-Temporal Attention Module (STAM) is designed in the network to adaptively aggregate the feature information of history frames to the current frame. Various structure of the STAM is also studied to ensure the best performance. Experiments on Tusimple and ApolloScape datasets show that our method can effectively improve lane mark detection and achieve state-of-the-art performance.