  Training deep models for lane detection is challenging due to the very subtle
and sparse supervisory signals inherent in lane annotations. Without learning
from much richer context, these models often fail in challenging scenarios,
e.g., severe occlusion, ambiguous lanes, and poor lighting conditions. In this
paper, we present a novel knowledge distillation approach, i.e., Self Attention
Distillation (SAD), which allows a model to learn from itself and gains
substantial improvement without any additional supervision or labels.
Specifically, we observe that attention maps extracted from a model trained to
a reasonable level would encode rich contextual information. The valuable
contextual information can be used as a form of 'free' supervision for further
representation learning through performing topdown and layer-wise attention
distillation within the network itself. SAD can be easily incorporated in any
feedforward convolutional neural networks (CNN) and does not increase the
inference time. We validate SAD on three popular lane detection benchmarks
(TuSimple, CULane and BDD100K) using lightweight models such as ENet, ResNet-18
and ResNet-34. The lightest model, ENet-SAD, performs comparatively or even
surpasses existing algorithms. Notably, ENet-SAD has 20 x fewer parameters and
runs 10 x faster compared to the state-of-the-art SCNN, while still achieving
compelling performance in all benchmarks. Our code is available at
https://github.com/cardwing/Codes-for-Lane-Detection.
