Lane Detection
Lane detection is the task of detecting lanes on a road from a camera.
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.
#4 best model for
Lane Detection
on TuSimple
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.
SOTA for Lane Detection on TuSimple
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.
SOTA for Lane Detection on TuSimple
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.
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.
#3 best model for
Lane Detection
on TuSimple
AUTONOMOUS DRIVING INSTANCE SEGMENTATION LANE DETECTION OBJECT DETECTION SEMANTIC SEGMENTATION
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.
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.
Copy-and-Paste Networks for Deep Video Inpainting
ICCV 2019
• shleecs/Copy-and-Paste-Networks-for-Deep-Video-Inpainting
•
We propose a novel DNN-based framework called the Copy-and-Paste Networks for video inpainting that takes advantage of additional information in other frames of the video.