Learning Lightweight Lane Detection CNNs by Self Attention Distillation

ICCV 2019 Yuenan HouZheng MaChunxiao LiuChen Change Loy

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... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Lane Detection BDD100k ENet-SAD Accuracy 36.56% # 1
Lane Detection CULane ENet-SAD F1 score 70.8 # 2
Lane Detection TuSimple ENet-SAD Accuracy 96.64% # 1