Method
Setting
Code
MaxF
AP
PRE
REC
FPR
FNR
Runtime
Environment
1
UNV
96.69 %
92.41 %
97.38 %
96.01 %
1.18 %
3.99 %
1.2 s
GPU @ 3.0 Ghz (Python + C/C++)
2
iDST-VT
96.66 %
93.65 %
96.55 %
96.76 %
1.57 %
3.24 %
1 s
GPU @ 2.5 Ghz (Python + C/C++)
3
VGGFCN-6D
96.64 %
92.85 %
96.37 %
96.91 %
1.66 %
3.09 %
.006 s
GPU @ 3.5 Ghz (Python)
4
NF2CNN
96.09 %
88.40 %
94.11 %
98.16 %
2.80 %
1.84 %
.006 s
GPU @ 3.5 Ghz (Python)
5
KRSF
96.02 %
93.60 %
95.61 %
96.44 %
2.02 %
3.56 %
0.3 s
GPU @ 2.5 Ghz (Python + C/C++)
6
KRS
95.89 %
93.51 %
95.79 %
95.99 %
1.92 %
4.01 %
0.3 s
GPU @ 2.5 Ghz (Python)
7
YhY
code
95.80 %
89.11 %
94.89 %
96.73 %
2.38 %
3.27 %
0.4 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
8
LidCamNet
95.62 %
93.54 %
95.77 %
95.48 %
1.92 %
4.52 %
0.15 s
GPU @ 2.5 Ghz (Python)
9
DFFA
95.58 %
89.30 %
95.10 %
96.06 %
2.25 %
3.94 %
0.4 s
GPU @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
10
MVnet
95.45 %
91.49 %
97.51 %
93.49 %
1.09 %
6.51 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
11
RSNet
95.28 %
92.43 %
95.22 %
95.35 %
2.18 %
4.65 %
0.09 s
GPU @ 2.5 Ghz (Python + C/C++)
12
WNet
95.24 %
93.04 %
96.01 %
94.48 %
1.79 %
5.52 %
0.1 s
4 cores @ 2.5 Ghz (Python)
13
BIRD
95.18 %
92.44 %
94.69 %
95.68 %
2.45 %
4.32 %
25 ms
GPU @ 2.5 Ghz (Python + C/C++)
14
TDCac1 CNN
94.86 %
89.62 %
95.45 %
94.28 %
2.05 %
5.72 %
.093 s
1 core @ 1.0 Ghz (C/C++)
15
RSNet-
94.84 %
92.83 %
94.32 %
95.37 %
2.62 %
4.63 %
0.07 s
GPU @ 2.5 Ghz (Python)
16
baseline
94.80 %
92.80 %
94.35 %
95.25 %
2.60 %
4.75 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
17
IDA-Fusion
94.77 %
88.03 %
93.71 %
95.86 %
2.93 %
4.14 %
0.1 s
4 cores @ 3.5 Ghz (C/C++)
18
RBNet
94.77 %
91.42 %
95.16 %
94.37 %
2.19 %
5.63 %
0.18 s
GPU @ 2.5 Ghz (Matlab + C/C++)
Z. Chen and Z. Chen: RBNet: A Deep Neural Network for Unified Road and
Road Boundary Detection . International Conference on Neural Information
Processing 2017.
19
WSLGAN
94.73 %
89.22 %
95.01 %
94.45 %
2.26 %
5.55 %
800ms
GPU @ 1.5 Ghz (Python)
20
MMN
94.72 %
92.51 %
94.84 %
94.60 %
2.34 %
5.40 %
0.1 s
GPU @ 2.5 Ghz (C/C++)
21
KRS
94.69 %
93.40 %
94.72 %
94.67 %
2.41 %
5.33 %
1 s
GPU @ 2.5 Ghz (Python)
22
RSNet2
94.65 %
92.54 %
94.45 %
94.85 %
2.54 %
5.15 %
0.07 s
GPU @ 2.5 Ghz (Python + C/C++)
23
SSLGAN
94.62 %
89.50 %
95.32 %
93.93 %
2.10 %
6.07 %
700ms
GPU @ 1.5 Ghz (Python)
24
FNETMS
94.51 %
92.72 %
94.92 %
94.11 %
2.30 %
5.89 %
0.04 s
GPU @ 2.0 Ghz (Python + C/C++)
25
DEEP-DIG
94.16 %
93.41 %
95.02 %
93.32 %
2.23 %
6.68 %
0.14 s
GPU @ 3.5 Ghz (Python + C/C++)
J. Muñoz-Bulnes, C. Fernandez, I. Parra, D. Fernández-Llorca and M. Sotelo: Deep Fully Convolutional
Networks with Random Data Augmentation
for Enhanced Generalization in Road
Detection . Workshop on Deep
Learning for Autonomous Driving on IEEE
20th International Conference on
Intelligent Transportation Systems 2017.
26
FusionNet
94.15 %
92.26 %
95.18 %
93.14 %
2.15 %
6.86 %
0.3 s
GPU @ 2.5 Ghz (Python + C/C++)
27
SPN
94.06 %
89.64 %
95.47 %
92.68 %
2.00 %
7.32 %
1 s
1 core @ 2.5 Ghz (C/C++)
28
StixelNet II
94.05 %
85.85 %
91.30 %
96.98 %
4.21 %
3.02 %
1.2 s
1 core @ 3.0 Ghz (Matlab + C/C++)
N. Garnett, S. Silberstein, S. Oron, E. Fetaya, U. Verner, A. Ayash, V. Goldner, R. Cohen, K. Horn and D. Levi: Real-time category-based and general
obstacle detection for autonomous driving . 5th Workshop on Computer Vision for
Road Scene Understanding and Autonomous Driving
(CVRSUAD'17, IEEE-ICCV 2017
Workshop) 2017.
29
MultiNet
code
93.99 %
93.24 %
94.51 %
93.48 %
2.47 %
6.52 %
0.17 s
GPU @ 2.5 Ghz (Python + C/C++)
M. Teichmann, M. Weber, J. Zoellner, R. Cipolla and R. Urtasun: MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving . CoRR 2016.
30
FDN
93.99 %
93.29 %
94.53 %
93.46 %
2.47 %
6.54 %
0.2 s
GPU 1 core @ 2.5 Ghz (Python)
31
CoDNN
93.90 %
92.86 %
94.51 %
93.29 %
2.47 %
6.71 %
0.01 s
1 core @ 2.5 Ghz (Python + C/C++)
32
FuseNet
93.86 %
93.34 %
94.49 %
93.23 %
2.48 %
6.77 %
0.2 s
GPU @ 2.5 Ghz (Python + C/C++)
33
FCN-GCBs
93.86 %
86.62 %
92.15 %
95.62 %
3.71 %
4.38 %
0.08 s
GPU @ 2.5 Ghz (C/C++)
34
ResNetPK
93.78 %
89.01 %
94.78 %
92.81 %
2.33 %
7.19 %
0.4s
GPU @ 1.5 Ghz (Python)
35
FCN_RGBD
93.78 %
93.40 %
94.71 %
92.86 %
2.36 %
7.14 %
0.4 s
1 core @ 2.5 Ghz (C/C++)
36
MBN
93.77 %
85.59 %
91.02 %
96.69 %
4.34 %
3.31 %
0.16 s
GPU @ 2.5 Ghz (Python)
37
DDN
93.65 %
88.55 %
94.28 %
93.03 %
2.57 %
6.97 %
2 s
GPU @ 2.5 Ghz (Python + C/C++)
R. Mohan: Deep Deconvolutional Networks for Scene Parsing . 2014.
38
wt
93.27 %
92.92 %
93.20 %
93.34 %
3.10 %
6.66 %
0.1 s
GPU @ 1.0 Ghz (Python)
39
RDSN
92.77 %
87.54 %
93.16 %
92.39 %
3.09 %
7.61 %
0.25 s
GPU @ 2.5 Ghz (Python)
40
LoDNN
92.75 %
89.98 %
90.09 %
95.58 %
4.79 %
4.42 %
18 ms
GPU @ 2.5 Ghz (Torch)
L. Caltagirone, S. Scheidegger, L. Svensson and M. Wahde: Fast LIDAR-based Road Detection
Using Fully Convolutional Neural Networks . IEEE Intelligent Vehicles
Symposium 2017.
41
SUNet
92.63 %
86.51 %
92.03 %
93.24 %
3.68 %
6.76 %
0.018s
42
DFN
92.32 %
88.26 %
91.79 %
92.85 %
3.78 %
7.15 %
0.25 s
GPU @ >3.5 Ghz (Python)
43
RSNetVGG
92.26 %
92.51 %
93.92 %
90.65 %
2.67 %
9.35 %
0.09 s
GPU @ 2.5 Ghz (Python + C/C++)
44
Up-Conv-Poly
code
92.20 %
88.85 %
92.57 %
91.83 %
3.36 %
8.17 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular
Road Segmentation . IROS 2016.
45
HID-LS
92.19 %
84.15 %
89.44 %
95.12 %
5.12 %
4.88 %
0.25 s
4 cores @ 3.0 Ghz (C/C++)
46
MixedCRF
91.57 %
84.68 %
90.02 %
93.19 %
4.71 %
6.81 %
6s
1 core @ 2.5 Ghz (Matlab + C/C++)
X. Han, H. Wang, J. Lu and C. Zhao: Road detection based on the fusion of
Lidar and image data . 2017.
47
LiDAR-SPHnet
91.50 %
81.98 %
87.05 %
96.42 %
6.53 %
3.58 %
0.14 s
GPU @ 1.5 Ghz (Matlab)
48
fcn_rgbd
91.25 %
92.30 %
89.86 %
92.68 %
4.77 %
7.32 %
0.6 s
1 core @ 2.5 Ghz (C/C++)
49
FTP
91.20 %
90.60 %
91.11 %
91.29 %
4.06 %
8.71 %
0.28 s
GPU @ 2.5 Ghz (C/C++)
A. Laddha, M. Kocamaz, L. Navarro-Serment and M. Hebert: Map-Supervised Road Detection . IEEE Intelligent Vehicles Symposium Proceedings 2016.
50
HybridCRF
90.99 %
85.26 %
90.65 %
91.33 %
4.29 %
8.67 %
1.5 s
1 core @ 2.5 Ghz (C/C++)
L. Xiao, R. Wang, B. Dai, Y. Fang, D. Liu and T. Wu: Hybrid Conditional Random Field based
Camera-LIDAR Fusion for Road Detection . Information Sciences. in press.
51
NNP
90.50 %
87.95 %
91.43 %
89.59 %
3.83 %
10.41 %
5 s
4 cores @ 2.5 Ghz (Matlab)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object
Class Detection . NIPS 2015.
52
Up-Conv
90.48 %
88.20 %
91.30 %
89.67 %
3.90 %
10.33 %
0.05 s
GPU @ 2.5 Ghz (C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular
Road Segmentation . IROS 2016.
53
TFSeg
90.12 %
89.85 %
88.00 %
92.36 %
5.74 %
7.64 %
0.07 s
GPU @ 1.0 Ghz (Python)
54
HIM
90.07 %
79.98 %
90.79 %
89.35 %
4.13 %
10.65 %
7 s
>8 cores @ 2.5 Ghz (Python + C/C++)
D. Munoz, J. Bagnell and M. Hebert: Stacked Hierarchical Labeling . European Conference on Computer Vision (ECCV) 2010.
55
LidarHisto
code
89.87 %
83.03 %
91.28 %
88.49 %
3.85 %
11.51 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
L. Chen, J. Yang and H. Kong: Lidar-histogram for fast road and obstacle
detection . 2017 IEEE International Conference on
Robotics and Automation (ICRA) 2017.
56
CNN-FCRF
89.87 %
83.38 %
88.58 %
91.18 %
5.35 %
8.82 %
1 s
4 cores @ 3.5 Ghz (C/C++)
57
FusedCRF
89.55 %
80.00 %
84.87 %
94.78 %
7.70 %
5.22 %
2 s
1 core @ 2.5 Ghz (C/C++)
L. Xiao, B. Dai, D. Liu, T. Hu and T. Wu: CRF based Road Detection with Multi-Sensor Fusion . Intelligent Vehicles Symposium (IV) 2015.
58
LWDS
89.50 %
86.11 %
90.59 %
88.44 %
4.19 %
11.56 %
0.07 s
GPU @ 2.5 Ghz (Python)
59
FCN-LC
89.36 %
78.80 %
89.35 %
89.37 %
4.85 %
10.63 %
0.03 s
GPU Titan X
C. Mendes, V. Frémont and D. Wolf: Exploiting Fully Convolutional Neural
Networks for Fast Road Detection . IEEE Conference on Robotics and
Automation (ICRA) 2016.
60
CB
88.89 %
82.17 %
87.26 %
90.58 %
6.03 %
9.42 %
2 s
1 core @ 3.4 Ghz (Python) + GPU
C. Mendes, V. Frémont and D. Wolf: Vision-Based Road Detection using
Contextual Blocks . 2015.
61
RD
88.87 %
82.04 %
93.31 %
84.83 %
2.77 %
15.17 %
4 s
1 core @ 3.0 Ghz (Matlab + C/C++)
62
HFM
88.83 %
80.33 %
85.23 %
92.75 %
7.32 %
7.25 %
5 s
2 cores @ 2.0 Ghz (C/C++)
63
DVFCN
88.64 %
91.37 %
89.20 %
88.10 %
4.86 %
11.90 %
0.07 s
GPU @ 2.5 Ghz (Python)
64
ResAXN
88.46 %
91.06 %
90.10 %
86.88 %
4.35 %
13.12 %
0.06 s
GPU @ 1.5 Ghz (Python)
65
SPRAY
88.14 %
91.24 %
88.60 %
87.68 %
5.14 %
12.32 %
45 ms
NVIDIA GTX 580 (Python + OpenCL)
T. Kuehnl, F. Kummert and J. Fritsch: Spatial Ray Features for Real-Time Ego-Lane Extraction . Proc. IEEE Intelligent Transportation Systems 2012.
66
ProbBoost
87.48 %
80.13 %
85.02 %
90.09 %
7.23 %
9.91 %
2.5 min
>8 cores @ 3.0 Ghz (C/C++)
G. Vitor, A. Victorino and J. Ferreira: A probabilistic distribution approach for the classification of urban roads in complex environments . Workshop on Modelling, Estimation, Perception and Control of All Terrain Mobile Robots on IEEE International Conference on Robotics and Automation (ICRA) 2014.
67
LWD
87.39 %
88.85 %
84.99 %
89.92 %
7.24 %
10.08 %
0.07 s
GPU @ 2.5 Ghz (Python)
68
MAP
87.33 %
89.62 %
85.77 %
88.95 %
6.73 %
11.05 %
0.28s
GPU
A. Laddha, M. Kocamaz, L. Navarro-Serment and M. Hebert: Map-Supervised Road Detection . IEEE Intelligent Vehicles Symposium Proceedings 2016.
69
Pos-ex
code
86.74 %
90.43 %
88.09 %
85.44 %
5.26 %
14.56 %
120 ms
GPU(K20) @ 0.7 Ghz (Matlab + Caffe)
70
CN24
86.32 %
89.19 %
87.80 %
84.89 %
5.37 %
15.11 %
30 s
>8 cores @ 2.5 Ghz (C/C++)
C. Brust, S. Sickert, M. Simon, E. Rodner and J. Denzler: Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding . VISAPP 2015 - Proceedings of the 10th International Conference on
Computer Vision Theory and Applications, Berlin, Germany,
11-14 March, 2015 2015.
71
GRES3D+VELO
85.43 %
83.04 %
82.69 %
88.37 %
8.43 %
11.63 %
60 ms
4 cores @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points . 2015.
72
StixelNet
85.33 %
72.14 %
81.21 %
89.89 %
9.48 %
10.11 %
1 s
GPU @ 2.5 Ghz (C/C++)
D. Levi, N. Garnett and E. Fetaya: StixelNet: A Deep Convolutional Network for
Obstacle Detection and Road Segmentation. . 26TH British Machine Vision Conference
(BMVC) 2015.
73
SPlane + BL
85.23 %
88.66 %
83.43 %
87.12 %
7.89 %
12.88 %
2 s
1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption . IV 2014.
74
CNN+BiGRU
85.21 %
79.80 %
90.57 %
80.44 %
3.82 %
19.56 %
20 ms
GPU @ GTX950M (Python +Tensorflow)
75
geo+gpr+crf
85.13 %
72.24 %
81.33 %
89.29 %
9.34 %
10.71 %
30 s
1 core @ 2.0 Ghz (C/C++)
Z. Xiao, B. Dai, H. Li, T. Wu, X. Xu, Y. Zeng and T. Chen: Gaussian process regression-based robust
free space detection for autonomous vehicle by 3-D
point cloud and 2-D appearance information fusion . International Journal of Advanced
Robotic Systems 2017.
76
CNN_LSTM
85.09 %
78.98 %
89.57 %
81.03 %
4.30 %
18.97 %
20 ms
GPU @ GTX950M (Python +Tensorflow)
77
Strait
84.28 %
87.89 %
83.66 %
84.91 %
7.56 %
15.09 %
69 ms
GPU @ K20
78
RES3D-Velo
83.81 %
73.95 %
78.56 %
89.80 %
11.16 %
10.20 %
0.36 s
1 core @ 2.5 Ghz (C/C++)
P. Shinzato, D. Wolf and C. Stiller: Road Terrain Detection: Avoiding Common Obstacle Detection Assumptions Using Sensor Fusion . Intelligent Vehicles Symposium (IV) 2014.
79
SCRFFPFHGSP
83.73 %
72.89 %
82.13 %
85.39 %
8.47 %
14.61 %
5 s
8 cores @ 2.5 Ghz (C/C++, Matlab)
I. Gheorghe: Semantic Segmentation of
Terrain and
Road Terrain for Advanced Driver
Assistance Systems . 2015.
80
GRES3D+SELAS
83.69 %
84.61 %
78.31 %
89.88 %
11.35 %
10.12 %
110 ms
4 core @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points . 2015.
81
HistonBoost
83.68 %
72.79 %
82.01 %
85.42 %
8.54 %
14.58 %
2.5 min
>8 cores @ 3.0 Ghz (C/C++)
G. Vitor, A. Victorino and J. Ferreira: Comprehensive Performance Analysis of Road Detection Algorithms Using the Common Urban Kitti-Road Benchmark . Workshop on Benchmarking Road Terrain and Lane Detection Algorithms for In-Vehicle Application on IEEE Intelligent Vehicles Symposium (IV) 2014.
82
FRS_SP
83.22 %
72.94 %
77.11 %
90.39 %
12.23 %
9.61 %
0.21 s
4 cores @ 3.0 Ghz (Matlab + C/C++)
83
SegNet
82.17 %
76.46 %
84.03 %
80.40 %
6.97 %
19.60 %
0.01 s
8 cores @ 2.5 Ghz (C/C++)
84
LKW
82.01 %
85.26 %
78.83 %
85.46 %
10.46 %
14.54 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
85
SP-SS
81.60 %
69.62 %
78.13 %
85.40 %
10.89 %
14.60 %
0.01 s
4 cores @ 3.0 Ghz (Matlab + C/C++)
86
PGM-ARS
80.97 %
69.11 %
77.51 %
84.76 %
11.21 %
15.24 %
0.05 s
i74700MQ @ 2.1Ghz (C/C++)
M. Passani, J. Yebes and L. Bergasa: Fast Pixelwise Road Inference Based on
Uniformly Reweighted Belief Propagation
. Proc. IEEE Intelligent
Vehicles Symposium 2015.
87
RES3D-Stereo
78.98 %
80.06 %
75.94 %
82.27 %
11.88 %
17.73 %
0.7 s
1 core @ 2.5 Ghz (C/C++)
P. Shinzato, D. Gomes and D. Wolf: Road estimation with sparse 3D points from stereo data . Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on 2014.
88
BM
78.90 %
66.06 %
69.53 %
91.19 %
18.21 %
8.81 %
2 s
2 cores @ 2.5 Ghz (Matlab)
B. WANG, V. Fremont and S. Rodriguez Florez: Color-based Road Detection and its
Evaluation on the KITTI Road Benchmark . Workshop on Benchmarking Road Terrain
and Lane Detection Algorithms for In-Vehicle
Application, IEEE Intelligent Vehicles Symposium 2014.
89
CNN_LSTM
78.42 %
69.11 %
83.28 %
74.09 %
6.78 %
25.91 %
54 ms
GPU @ 2.0 Ghz (Python + C/C++)
90
SPlane
78.19 %
76.41 %
72.03 %
85.50 %
15.13 %
14.50 %
2 s
1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption . IV 2014.
91
SRF
76.43 %
83.24 %
75.53 %
77.35 %
11.42 %
22.65 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
L. Xiao, B. Dai, D. Liu, D. Zhao and T. Wu: Monocular Road Detection Using Structured
Random Forest . Int J Adv Robot Syst 2016.
92
CN24
76.28 %
79.29 %
72.44 %
80.55 %
13.96 %
19.45 %
20 s
>8 cores @ 2.5 Ghz (C/C++)
C. Brust, S. Sickert, M. Simon, E. Rodner and J. Denzler: Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding . VISAPP 2015 - Proceedings of the 10th International Conference on
Computer Vision Theory and Applications, Berlin, Germany,
11-14 March, 2015 2015.
93
CN
73.69 %
76.68 %
69.18 %
78.83 %
16.00 %
21.17 %
2 s
1 core @ 2.5 Ghz (C/C++)
J. Alvarez, T. Gevers, Y. LeCun and A. Lopez: Road Scene Segmentation from a Single Image . ECCV 2012 2012.
94
ARSL-AMI
71.97 %
61.04 %
78.03 %
66.79 %
8.57 %
33.21 %
0.05 s
4 cores @ 2.5 Ghz (C/C++)
M. Passani, J. Yebes and L. Bergasa: CRF-based semantic labeling in
miniaturized road scenes
. Proc. IEEE Intelligent
Transportation Systems 2014.
95
ANN
62.83 %
46.77 %
50.21 %
83.91 %
37.91 %
16.09 %
3 s
1 core @ 3.0 Ghz (C/C++)
G. Vitor, D. Lima, A. Victorino and J. Ferreira: A 2D/3D Vision Based Approach Applied to Road Detection in Urban Environments . Intelligent Vehicles Symposium (IV), 2013 IEEE 2013.
96
VAP
59.23 %
42.05 %
44.44 %
88.75 %
50.55 %
11.25 %
1 s
1 core @ 2.5 Ghz (Matlab)
ERROR: Wrong syntax in BIBTEX file.