Lane detection is one of the core functions in autonomous driving and has
aroused widespread attention recently. The networks to segment lane instances,
especially with bad appearance, must be able to explore lane distribution
properties. Most existing methods tend to resort to CNN-based techniques. A few
have a try on incorporating the recent adorable, the seq2seq Transformer
\cite{transformer}. However, their innate drawbacks of weak global information
collection ability and exorbitant computation overhead prohibit a wide range of
the further applications. In this work, we propose Atrous Transformer
(AtrousFormer) to solve the problem. Its variant local AtrousFormer is
interleaved into feature extractor to enhance extraction. Their collecting
information first by rows and then by columns in a dedicated manner finally
equips our network with stronger information gleaning ability and better
computation efficiency. To further improve the performance, we also propose a
local semantic guided decoder to delineate the identities and shapes of lanes
more accurately, in which the predicted Gaussian map of the starting point of
each lane serves to guide the process. Extensive results on three challenging
benchmarks (CULane, TuSimple, and BDD100K) show that our network performs
favorably against the state of the arts.

提出了一种名为 Atrous Transformer 的神经网络架构来进行车道检测，该网络采用局部 Atrous Former 增强特征提取器，然后通过行列顺序收集信息以提高信息提取能力，此外还提出了一种局部语义引导解码器来更准确地描绘车道的身份和形状，并使用三个基准测试数据集对其进行了评估，结果表明该网络表现优越。