baseline

Baseline

Not exactly proposed by any paper.

Method Overview

The segmentation baseline takes semantic segmentation networks and appends a lane existence head. It takes the most classic multi-class segmentation approach, its design originates from the SCNN paper (ResNets and VGG based Deeplab), while the SAD paper explores the use of ENet and ERFNet, later the RESA paper reduced the network width for efficient ResNet baselines, finally the BézierLaneNet paper (this framework) improved these baselines with modern training techniques and fair evaluations, further extended them to modern architectures such as Swin Transformer, RepVGG and MobileNets. Among them, the ERFNet baseline even achieves comparable performance against SOTA methods. However, they are very sensitive to hyper-parameters, see Wiki and the BézierLaneNet Appendix.B for more info. Specifically, the VGG16 backbone corresponds to DeepLab-LargeFOV in SCNN, the ResNet & other backbones correspond to DeepLabV2 (w.o. ASPP) with output channels reduced to 128 as in RESA. We sometimes call them by backbone names for consistency with common practices.

Results

Training time estimated with single 2080 Ti.

ImageNet pre-training, 3-times average/best.

⁺ Measured on a single GTX 1080Ti.

^# No pre-training.

* Trained on a 1080 Ti cluster, with CUDA 9.0 PyTorch 1.3, training time is estimated as: single 2080 Ti, mixed precision.

** Trained on two 2080ti.

TuSimple (test)

backbone	aug	resolution	training time	precision	accuracy (avg)	accuracy	FP	FN
VGG16	level 0	360 x 640	1.5h	mix	93.79%	93.94%	0.0998	0.1021	model | shell
ResNet18	level 0	360 x 640	0.7h	mix	94.18%	94.25%	0.0881	0.0894	model | shell
ResNet34	level 0	360 x 640	1.1h	mix	95.23%	95.31%	0.0640	0.0622	model | shell
ResNet34	level 1a	360 x 640	1.2h*	full	92.14%	92.68%	0.1073	0.1221	model | shell
ResNet50	level 0	360 x 640	1.5h	mix	95.07%	95.12%	0.0649	0.0653	model | shell
ResNet101	level 0	360 x 640	2.6h	mix	95.15%	95.19%	0.0619	0.0620	model | shell
ERFNet	level 0	360 x 640	0.8h	mix	96.02%	96.04%	0.0591	0.0365	model | shell
ERFNet	level 1a	360 x 640	0.9h*	full	94.21%	94.37%	0.0846	0.0770	model | shell
ENet#	level 0	360 x 640	1h+	mix	95.55%	95.61%	0.0655	0.0503	model | shell
MobileNetV2	level 0	360 x 640	0.5h	mix	93.98%	94.07%	0.0792	0.0866	model | shell
MobileNetV3-Large	level 0	360 x 640	0.5h	mix	92.09%	92.18%	0.1149	0.1322	model | shell

CULane (test)

backbone	aug	resolution	training time	precision	F1 (avg)	F1	normal	crowded	night	no line	shadow	arrow	dazzle light	curve	crossroad
VGG16	level 0	288 x 800	9.3h	mix	65.93	66.09	85.51	64.05	61.14	35.96	59.76	78.43	53.25	62.16	2224	model | shell
ResNet18	level 0	288 x 800	5.3h	mix	65.19	65.30	85.45	62.63	61.04	33.88	51.72	78.15	53.05	59.70	1915	model | shell
ResNet34	level 0	288 x 800	7.3h	mix	69.82	69.92	89.46	66.66	65.38	40.43	62.17	83.18	58.51	63.00	1713	model | shell
ResNet50	level 0	288 x 800	12.4h	mix	68.31	68.48	88.15	65.73	63.74	37.96	62.59	81.68	59.47	64.01	2046	model | shell
ResNet101	level 0	288 x 800	20.0h	mix	71.29	71.37	90.11	67.89	67.01	43.10	70.56	85.09	61.77	65.47	1883	model | shell
ERFNet	level 0	288 x 800	6h	mix	73.40	73.49	91.48	71.27	68.09	46.76	74.47	86.09	64.18	66.89	2102	model | shell
ENet#	level 0	288 x 800	6.4h+	mix	69.39	69.90	89.26	68.15	62.99	42.43	68.59	83.10	58.49	63.23	2464	model | shell
MobileNetV2	level 0	288 x 800	3.0h	mix	67.34	67.41	87.82	65.09	61.46	38.15	57.34	79.29	55.89	60.29	2114	model | shell
MobileNetV3-Large	level 0	288 x 800	3.0h	mix	68.27	68.42	88.20	66.33	63.08	40.41	56.15	79.81	59.15	61.96	2304	model | shell
RepVGG-A0	level 0	288 x 800	3.3h**	mix	70.22	70.56	89.74	67.68	65.21	42.51	67.85	83.13	60.86	63.63	2011	model | shell
RepVGG-A1	level 0	288 x 800	4.1h**	mix	70.73	70.85	89.92	68.60	65.43	41.99	66.64	84.78	61.38	64.85	2127	model | shell
RepVGG-B0	level 0	288 x 800	6.2h**	mix	71.77	71.81	90.86	69.32	66.68	43.53	67.83	85.43	59.80	66.47	2189	model | shell
RepVGG-B1g2	level 0	288 x 800	10.0h**	mix	72.08	72.20	90.85	69.31	67.94	43.81	68.45	85.85	60.64	67.69	2092	model | shell
RepVGG-B2	level 0	288 x 800	13.2h**	mix	72.24	72.33	90.82	69.84	67.65	43.02	72.08	85.76	61.75	67.67	2000	model | shell
Swin-Tiny	level 0	288 x 800	12.1h**	mix	69.75	69.90	89.55	68.36	63.56	42.53	61.96	82.64	60.81	65.21	2813	model | shell

LLAMAS (val)

backbone	aug	resolution	training time	precision	F1 (avg)	F1	TP	FP	FN	Precision	Recall
VGG16	level 0	360 x 640	9.3h	mix	95.05	95.11	70263	3460	3772	95.31	94.91	model | shell
ResNet34	level 0	360 x 640	7.0h	mix	95.90	95.91	70841	2847	3194	96.14	95.69	model | shell
ERFNet	level 0	360 x 640	10.9h+	mix	95.94	96.13	71136	2830	2899	96.17	96.08	model | shell

Their test performance can be found at the LLAMAS leaderboard.

Profiling

FPS is best trial-avg among 3 trials on a 2080 Ti. Post-processing is ignored.

backbone	resolution	FPS	FLOPS(G)	Params(M)
VGG16	360 x 640	56.36	214.50	20.37
ResNet18	360 x 640	148.59	85.24	12.04
ResNet34	360 x 640	79.97	159.60	22.15
ResNet50	360 x 640	50.58	177.62	24.57
ResNet101	360 x 640	27.41	314.36	43.56
ERFNet	360 x 640	85.87	26.32	2.67
ENet	360 x 640	56.63	4.26	0.95
MobileNetV2	360 x 640	126.54	4.49	2.06
MobileNetV3-Large	360 x 640	104.34	3.63	3.30
VGG16	288 x 800	55.31	214.50	20.15
ResNet18	288 x 800	136.28	85.22	11.82
ResNet34	288 x 800	72.42	159.60	21.93
ResNet50	288 x 800	49.41	177.60	24.35
ResNet101	288 x 800	27.19	314.34	43.34
ERFNet	288 x 800	88.76	26.26	2.68
ENet	288 x 800	57.99	4.12	0.96
MobileNetV2	288 x 800	129.24	4.41	2.00
MobileNetV3-Large	288 x 800	107.83	3.56	3.25
RepVGG-A0	288 x 800	162.61	207.81	9.06
RepVGG-A1	288 x 800	117.30	339.83	13.54
RepVGG-B0	288 x 800	103.68	390.83	15.09
RepVGG-B1g2	288 x 800	36.91	1166.76	42.20
RepVGG-B2	288 x 800	18.98	2310.13	81.23
Swin-Tiny	288 x 800	51.90	44.24	27.72

Citation (if you have to)

@inproceedings{pan2018spatial,
  title={Spatial as deep: Spatial cnn for traffic scene understanding},
  author={Pan, Xingang and Shi, Jianping and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
  booktitle={AAAI},
  year={2018}
}

@inproceedings{feng2022rethinking,
  title={Rethinking efficient lane detection via curve modeling},
  author={Feng, Zhengyang and Guo, Shaohua and Tan, Xin and Xu, Ke and Wang, Min and Ma, Lizhuang},
  booktitle={Computer Vision and Pattern Recognition},
  year={2022}
}