Lane Detection Edit Task
Computer Vision • Autonomous Vehicles
About
Lane detection is the task of detecting lanes on a road from a camera.
( Image credit: End-to-end Lane Detection )
Benchmarks
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Datasets
Greatest papers with code
Towards End-to-End Lane Detection: an Instance Segmentation Approach
• MaybeShewill-CV/lanenet-lane-detection
• 
By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, pre-defined transformation.
Ranked #6 on
Lane Detection
on TuSimple
End to End Learning for Self-Driving Cars
The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal.
Learning Lightweight Lane Detection CNNs by Self Attention Distillation
ICCV 2019
• cardwing/Codes-for-Lane-Detection
•
Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals inherent in lane annotations.
Ranked #1 on
Lane Detection
on BDD100k
KNOWLEDGE DISTILLATION LANE DETECTION REPRESENTATION LEARNING
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
• cardwing/Codes-for-Lane-Detection
•
Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored.
Ranked #4 on
Lane Detection
on TuSimple
(using extra training data)
Ultra Fast Structure-aware Deep Lane Detection
ECCV 2020
• cfzd/Ultra-Fast-Lane-Detection
• 
Modern methods mainly regard lane detection as a problem of pixel-wise segmentation, which is struggling to address the problem of challenging scenarios and speed.
Ranked #11 on
Lane Detection
on CULane
End-to-end Lane Detection through Differentiable Least-Squares Fitting
• wvangansbeke/LaneDetection_End2End
•
The problem with such a two-step approach is that the parameters of the network are not optimized for the true task of interest (estimating the lane curvature parameters) but for a proxy task (segmenting the lane markings), resulting in sub-optimal performance.
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions.
Ranked #1 on
Lane Detection
on Caltech Lanes Washington
Semantic Instance Segmentation with a Discriminative Loss Function
• Wizaron/instance-segmentation-pytorch
•
In this work we propose to tackle the problem with a discriminative loss function, operating at the pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step.
Ranked #4 on
Multi-Human Parsing
on MHP v1.0
INSTANCE SEGMENTATION LANE DETECTION METRIC LEARNING MULTI-HUMAN PARSING SEMANTIC SEGMENTATION
End-to-end Lane Shape Prediction with Transformers
• liuruijin17/LSTR
•
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
Learning to Cluster for Proposal-Free Instance Segmentation
• GT-RIPL/L2C
•
We utilize the most fundamental property of instance labeling -- the pairwise relationship between pixels -- as the supervision to formulate the learning objective, then apply it to train a fully convolutional network (FCN) for learning to perform pixel-wise clustering.
Ranked #5 on
Lane Detection
on TuSimple
AUTONOMOUS DRIVING INSTANCE SEGMENTATION LANE DETECTION OBJECT DETECTION SEMANTIC SEGMENTATION