Abstract
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.
Highlights
With the rapid development of autonomous driving technology, lane mark detection have made great progress in recent years
As detecting lane marks from individual images suffers from challenging situations such as heavy shadow, serious occlusion, and severe lane mark damage, we focus on lane mark detection under continuous driving scenes
Lane marks with an accuracy greater than 85% are considered as True Positive (TP), otherwise False Positive (FP) or False Negative (FN)
Summary
With the rapid development of autonomous driving technology, lane mark detection have made great progress in recent years. Traditional methods for lane mark detection usually involve several basic procedures, including image pre-processing, feature extraction, and detection by fitting [1,2,3]. Lane marks that cannot be precisely detected in a current single frame is able to be inferred from the information of former frames. A novel method using multiple frames for improving lane mark detection is proposed. With richer information from continuous images, the proposed method is able to greatly improve lane mark predictions on challenging scenarios and achieve state-of-the-art performance. By exploring the spatial-temporal information hidden in the multiple frames, the negative influence from complex scenarios like shadow, lane mark degradation, and vehicle occlusion could be largely mitigated;. The module enhances the features of current frame by attentively aggregating spatial-temporal information from history frames. Comprehensive experiments and ablation studies verified that the proposed model is effective and can achieve state-of-the-arts performance
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