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홈
SOTA
Lane Detection
Lane Detection On Curvelanes
Lane Detection On Curvelanes
평가 지표
F1 score
GFLOPs
Precision
Recall
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
F1 score
GFLOPs
Precision
Recall
Paper Title
Repository
CANet-S
86.57
13.1
91.37
82.25
CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection
-
CANet-M
87.19
22.6
91.53
83.25
CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection
-
Enet-SAD
50.31
3.9
63.6
41.6
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
CondLaneNet-L(ResNet-101)
86.10
44.9
88.98
83.41
CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution
CANet-L(ResNet101)
-
45.7
-
84.36
CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection
-
CLRNet-DLA34
86.1
18.4
91.4
81.39
CLRerNet: Improving Confidence of Lane Detection with LaneIoU
SCNN
65.02
328.4
76.13
56.74
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
CANet-L
87.87
-
91.69
-
CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection
-
CurveLane-S
81.12
7.4
93.58
71.59
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
CondLSTR (ResNet-101)
88.47
-
-
-
Generating Dynamic Kernels via Transformers for Lane Detection
CondLSTR (ResNet-18)
87.99
-
-
-
Generating Dynamic Kernels via Transformers for Lane Detection
CLRerNet-DLA34
86.47
18.4
91.66
81.83
CLRerNet: Improving Confidence of Lane Detection with LaneIoU
PointLaneNet
78.47
14.8
86.33
72.91
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
CurveLane-M
81.8
11.6
93.49
72.71
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
CondLaneNet-M(ResNet-34)
85.92
19.7
88.29
83.68
CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution
CondLSTR (ResNet-34)
88.23
-
-
-
Generating Dynamic Kernels via Transformers for Lane Detection
CurveLane-L
82.29
20.7
91.11
75.03
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
CondLaneNet-S(ResNet-18)
85.09
10.3
87.75
82.58
CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution
0 of 18 row(s) selected.
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