LaneAF: Robust Multi-Lane Detection with Affinity Fields

22 Mar 2021  ·  Hala Abualsaud, Sean Liu, David Lu, Kenny Situ, Akshay Rangesh, Mohan M. Trivedi ·

This study presents an approach to lane detection involving the prediction of binary segmentation masks and per-pixel affinity fields. These affinity fields, along with the binary masks, can then be used to cluster lane pixels horizontally and vertically into corresponding lane instances in a post-processing step... This clustering is achieved through a simple row-by-row decoding process with little overhead; such an approach allows LaneAF to detect a variable number of lanes without assuming a fixed or maximum number of lanes. Moreover, this form of clustering is more interpretable in comparison to previous visual clustering approaches, and can be analyzed to identify and correct sources of error. Qualitative and quantitative results obtained on popular lane detection datasets demonstrate the model's ability to detect and cluster lanes effectively and robustly. Our proposed approach performs on par with state-of-the-art approaches on the limited TuSimple benchmark, and sets a new state-of-the-art on the challenging CULane dataset. read more

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Lane Detection CULane LaneAF F1 score 77.25 # 2
Lane Detection TuSimple LaneAF Accuracy 95.64% # 16
F1 score 96.42 # 6

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