Table 7. Comparative experimental results adopting ResNet34 as the backbone on CULane dataset.

Bold numbers are the best results.

Method	Backbone	F1 (%)	FPS	Normal (%)	Crowd (%)	Dazzle (%)	Shadow (%)	No line (%)	Arrow (%)	Curve (%)	Cross	Night (%)
SCNN (Pan et al., 2018)	VGG16	71.60	7.5	90.60	69.70	58.50	66.90	43.40	84.10	64.40	1990	66.10
LaneAF (Abualsaud et al., 2021)	ERFNet	75.63	24	91.10	73.32	69.71	75.81	50.62	86.86	65.02	1844	70.90
LaneAF (Abualsaud et al., 2021)	DLA-34	77.41	20	91.80	75.61	71.78	79.12	51.38	86.88	72.70	1360	73.03
FOLOLane (Qu et al., 2021)	ERFNet	78.80	40	92.70	77.80	75.20	79.30	52.10	89.00	69.40	1569	74.50
RESA (Zheng et al., 2021)	Resnet34	74.50	45.5	91.90	72.40	66.50	72.00	46.30	88.10	68.60	1896	69.80
GANet-m (Morley et al., 2001)	Resnet34	79.39	127	93.73	77.92	71.64	79.49	52.63	90.37	76.32	1368	73.67
LaneFormer (Han et al., 2022)	Resnet34	74.70	–	90.74	72.31	69.12	71.57	47.37	85.07	65.90	26	67.77
LaneATT (Tabelini et al., 2021a)	Resnet34	76.68	171	92.14	75.03	66.47	78.15	49.39	88.38	67.72	1330	70.72
CondLane (Liu et al., 2021a)	Resnet34	78.74	128	93.38	77.14	71.17	79.93	51.85	89.89	73.88	1387	73.92
CLRNet (Zheng et al., 2022)	Resnet34	79.73	103	93.49	78.06	74.57	79.92	54.01	90.59	72.77	1216	75.02
Our method	Resnet34	79.94	126	93.70	78.24	74.81	81.21	54.21	90.74	73.92	1160	74.85