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Lane Detection On Tusimple

Metrics

Accuracy
F1 score

Results

Performance results of various models on this benchmark

Paper Title
SCNN_UNet_Attention_PL*98.38-Robust Lane Detection through Self Pre-training with Masked Sequential Autoencoders and Fine-tuning with Customized PolyLoss
PE-RESA96.93-Lane detection with Position Embedding
FOLOLane(ERFNet)96.92-Focus on Local: Detecting Lane Marker from Bottom Up via Key Point
CLRNet(ResNet-34)96.9%97.82CLRNet: Cross Layer Refinement Network for Lane Detection
CLLD96.82-Contrastive Learning for Lane Detection via cross-similarity
CLRNet(ResNet-18)96.82%97.89CLRNet: Cross Layer Refinement Network for Lane Detection
RESA96.8296.93RESA: Recurrent Feature-Shift Aggregator for Lane Detection
CANet-L(ResNet101)96.76%97.77CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection
CANet-M96.66%97.44CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection
ENet-SAD96.64%95.92Learning Lightweight Lane Detection CNNs by Self Attention Distillation
HarD-SP96.58%96.38Towards Lightweight Lane Detection by Optimizing Spatial Embedding
CANet-S96.56%97.51CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection
CondLaneNet-L(ResNet-101)96.54%97.24CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution
Pairwise pixel supervision + FCN96.50%94.31Learning to Cluster for Proposal-Free Instance Segmentation
Oblique Convolution96.50%97.42-
EL-GAN96.40%96.26EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
LaneNet96.4%94.80Towards End-to-End Lane Detection: an Instance Segmentation Approach
Discriminative loss function96.40%-Semantic Instance Segmentation with a Discriminative Loss Function
ENet-Label96.29%95.23Agnostic Lane Detection
R-34-E2E96.22%96.58End-to-End Lane Marker Detection via Row-wise Classification
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