Lane detection is the task of detecting lanes on a road from a camera.
( Image credit: End-to-end Lane Detection )
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This study presents an approach to lane detection involving the prediction of binary segmentation masks and per-pixel affinity fields.
Ranked #1 on Lane Detection on CULane
Susceptibility of neural networks to adversarial attack prompts serious safety concerns for lane detection efforts, a domain where such models have been widely applied.
To tackle these issues, we propose an end-to-end method that directly outputs parameters of a lane shape model, using a network built with a transformer to learn richer structures and context.
Ranked #9 on Lane Detection on TuSimple
Modern lane detection methods have achieved remarkable performances in complex real-world scenarios, but many have issues maintaining real-time efficiency, which is important for autonomous vehicles.
Ranked #2 on Lane Detection on CULane
In this paper, we present a real-time robust neural network output enhancement for active lane detection (RONELD) method to identify, track, and optimize active lanes from deep learning probability map outputs.
Lane detection is one of the most important tasks in self-driving.
Ranked #1 on Lane Detection on TuSimple
This paper addresses the problem that pixel embedding in proposal-free instance segmentation based lane detection is difficult to optimize.
Ranked #3 on Lane Detection on TuSimple
One is used to extract the information of the most likely low-level features of lane markings.