Lane Detection
Lane detection is the task of detecting lanes on a road from a camera.
State-of-the-art leaderboards
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Greatest papers with code
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.
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.
#3 best model for
Lane Detection
on TuSimple
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
• XingangPan/SCNN
• 
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.
SOTA for Lane Detection on TuSimple
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.
Agnostic Lane Detection
• cardwing/Codes-for-Lane-Detection
•
Lane detection is an important yet challenging task in autonomous driving, which is affected by many factors, e. g., light conditions, occlusions caused by other vehicles, irrelevant markings on the road and the inherent long and thin property of lanes.
#2 best model for
Lane Detection
on CULane
AUTONOMOUS DRIVING INSTANCE SEGMENTATION LANE DETECTION MULTI-TASK LEARNING SEMANTIC SEGMENTATION
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.
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.
#2 best model for
Lane Detection
on TuSimple
AUTONOMOUS DRIVING INSTANCE SEGMENTATION LANE DETECTION OBJECT DETECTION SEMANTIC SEGMENTATION
Enhanced free space detection in multiple lanes based on single CNN with scene identification
• fabvio/ld-lsi
•
Traditional algorithms usually estimate only the position of the lanes on the road, but an autonomous control system may also need to know if a lane marking can be crossed or not, and what portion of space inside the lane is free from obstacles, to make safer control decisions.
LaneNet: Real-Time Lane Detection Networks for Autonomous Driving
Lane detection is to detect lanes on the road and provide the accurate location and shape of each lane.
Semantic Instance Segmentation with a Discriminative Loss Function
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.
#3 best model for
Lane Detection
on TuSimple
INSTANCE SEGMENTATION LANE DETECTION METRIC LEARNING MULTI-HUMAN PARSING SEMANTIC SEGMENTATION