Sparse Laneformer
Lane detection is a fundamental task in autonomous driving, and has achieved
great progress as deep learning emerges. Previous anchor-based methods often
design dense anchors, which highly depend on the training dataset and remain
fixed during inference. We analyze that dense anchors are not necessary for
lane detection, and propose a transformer-based lane detection framework based
on a sparse anchor mechanism. To this end, we generate sparse anchors with
position-aware lane queries and angle queries instead of traditional explicit
anchors. We adopt Horizontal Perceptual Attention (HPA) to aggregate the lane
features along the horizontal direction, and adopt Lane-Angle Cross Attention
(LACA) to perform interactions between lane queries and angle queries. We also
propose Lane Perceptual Attention (LPA) based on deformable cross attention to
further refine the lane predictions. Our method, named Sparse Laneformer, is
easy-to-implement and end-to-end trainable. Extensive experiments demonstrate
that Sparse Laneformer performs favorably against the state-of-the-art methods,
e.g., surpassing Laneformer by 3.0% F1 score and O2SFormer by 0.7% F1 score
with fewer MACs on CULane with the same ResNet-34 backbone.
66189b3e11548294cc9d0eb3
Ji Liu
66189b3e11548294cc9d0eb4
Zifeng Zhang
admin_assigned
2024-04-18T12:36:39.676Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64fff28e2ad36636be7e3297/ZSqhfQGhvOOkVscz-XHT-.jpeg
ZIFENG ZHANG
user
ConstStaticVoid
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Mingjie Lu
admin_assigned
2024-04-18T12:36:31.262Z
6539d1322a25dcfb561a2031
/avatars/d61e59d440741d3ee64df840f2b3d0fb.svg
MingjieLu
user
mingjiel
66189b3e11548294cc9d0eb6
Hongyang Wei
admin_assigned
2024-04-18T12:36:22.820Z
6539d1bf43d9189cdce4b9cd
/avatars/8a8dbcaba7501837915f4e5279e113cb.svg
Hongyang Wei
user
hongywei
66189b3e11548294cc9d0eb7
Dong Li
66189b3e11548294cc9d0eb8
Yile Xie
66189b3e11548294cc9d0eb9
Jinzhang Peng
admin_assigned
2024-04-18T12:37:09.906Z
6539e31d7ba797097a946302
/avatars/95f091ad3584a0abc95066658f4e7d52.svg
Jinzhang Peng
user
PengJinzhang
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Lu Tian
66189b3e11548294cc9d0ebb
Ashish Sirasao
66189b3e11548294cc9d0ebc
Emad Barsoum
admin_assigned
2024-04-18T12:37:37.693Z
65adc9d086f88a686be41215
https://cdn-avatars.huggingface.co/v1/production/uploads/65adc9d086f88a686be41215/xizVHuZPkE0Gu8_ulx0Fm.jpeg
Emad Barsoum
user
ebarsoum
Sparse Laneformer
Lane detection is a fundamental task in autonomous driving, and has achieved
great progress as deep learning emerges. Previous anchor-based methods often
design dense anchors, which highly depend on the training dataset and remain
fixed during inference. We analyze that dense anchors are not necessary for
lane detection, and propose a transformer-based lane detection framework based
on a sparse anchor mechanism. To this end, we generate sparse anchors with
position-aware lane queries and angle queries instead of traditional explicit
anchors. We adopt Horizontal Perceptual Attention (HPA) to aggregate the lane
features along the horizontal direction, and adopt Lane-Angle Cross Attention
(LACA) to perform interactions between lane queries and angle queries. We also
propose Lane Perceptual Attention (LPA) based on deformable cross attention to
further refine the lane predictions. Our method, named Sparse Laneformer, is
easy-to-implement and end-to-end trainable. Extensive experiments demonstrate
that Sparse Laneformer performs favorably against the state-of-the-art methods,
e.g., surpassing Laneformer by 3.0% F1 score and O2SFormer by 0.7% F1 score
with fewer MACs on CULane with the same ResNet-34 backbone.
A transformer-based lane detection framework using sparse anchors outperforms state-of-the-art methods with fewer computations.
transformer-based lane detection sparse anchors position-aware lane queries angle queries Horizontal Perceptual Attention Lane-Angle Cross Attention Lane Perceptual Attention deformable cross attention Sparse Laneformer F1 score MACs CULane ResNet-34