Lane Detection on CULane (test) (Robustness Evaluation) benchmark leaderboard · SOTA2 Research

Lane Detection on CULane (test) (Robustness Evaluation)

85.51Benign AccuracyBadNets

Evaluation Results

Method	Links
BadNets2025.08		85.51	79.6	50.13	54.33	61.39	55.28
Blended2025.08		85.51	74.37	43.67	56.42	56.51	52.2
LD-Attack2025.08		85.51	79.14	59.47	63.39	67.15	63.34
BadLANE2025.08		85.51	85.46	36.61	61.16	65.95	54.57
DBALD2025.08		85.51	82.37	66.49	64.82	68.17	66.5
BadNets2025.08		78.98	76.68	71.08	58.68	69.14	66.3
Blended2025.08		78.98	75.87	73.85	59.64	57.76	63.75
LD-Attack2025.08		78.98	71.35	73.26	64.77	69.18	69.07
BadLANE2025.08		78.98	72.81	75.39	62.94	68.23	68.85
DBALD2025.08		78.98	77.39	78.15	65.15	76.86	73.39
BadNets2025.08		75.01	72.79	49.27	43.37	47.36	46.67
Blended2025.08		75.01	73.15	57.14	59.07	47.04	54.41
LD-Attack2025.08		75.01	72.48	71.24	56.24	63.71	63.73
BadLANE2025.08		75.01	73.78	64.33	45.22	54.15	54.56
DBALD2025.08		75.01	74.2	81.65	73.45	74.19	76.41
BadNets2025.08		69.75	69.52	74.39	63.63	62.76	66.93
Blended2025.08		69.75	63.98	75.14	63.45	61.6	66.73
LD-Attack2025.08		69.75	64.38	74.83	62.21	64.17	67.07
BadLane2025.08		69.75	67.57	72.28	63.96	62.94	66.39
DBALD2025.08		69.75	68.75	76.19	67.34	68.69	70.74