3D Lane Detection With Attention in Attention
Lane detection is a critical component of autonomous driving systems and has been the subject of extensive research. Unlike object detection, identifying car lanes requires extracting features from multi-scale information since they are slender, sparse, and distributed in the entire image. Unfortuna...
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| Veröffentlicht in: | IEEE signal processing letters Jg. 31; S. 1104 - 1108 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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IEEE
2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | Lane detection is a critical component of autonomous driving systems and has been the subject of extensive research. Unlike object detection, identifying car lanes requires extracting features from multi-scale information since they are slender, sparse, and distributed in the entire image. Unfortunately, many previous works have overlooked this requirement, resulting in less robust lane feature extraction. To address this issue, we propose an attention mechanism that is exceptional at extracting global and long-dependence lane features compared to CNNs. Our attention-based structure, called attention in attention , explores the relationship between various correlations and mitigates mismatched correlation problems in attention computation. Furthermore, we introduce a novel feature fusion structure in the backbone called the double feature pyramid network, which effectively gathers feature information with various dimensions and enlarges the receptive field. Our network is based on the BEV-LaneDet and achieves impressive performance on the OpenLane dataset. Notably, experiments demonstrate that our method surpasses BEV-LaneDet by 4.4% in terms of F-Score on OpenLane. |
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| AbstractList | Lane detection is a critical component of autonomous driving systems and has been the subject of extensive research. Unlike object detection, identifying car lanes requires extracting features from multi-scale information since they are slender, sparse, and distributed in the entire image. Unfortunately, many previous works have overlooked this requirement, resulting in less robust lane feature extraction. To address this issue, we propose an attention mechanism that is exceptional at extracting global and long-dependence lane features compared to CNNs. Our attention-based structure, called attention in attention , explores the relationship between various correlations and mitigates mismatched correlation problems in attention computation. Furthermore, we introduce a novel feature fusion structure in the backbone called the double feature pyramid network, which effectively gathers feature information with various dimensions and enlarges the receptive field. Our network is based on the BEV-LaneDet and achieves impressive performance on the OpenLane dataset. Notably, experiments demonstrate that our method surpasses BEV-LaneDet by 4.4% in terms of F-Score on OpenLane. |
| Author | Li, Qian Yang, Xiaokang Gu, Yinchao Ma, Chao |
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| Cites_doi | 10.1109/CVPR.2017.106 10.1007/978-3-031-19839-7_32 10.1109/ICRA48891.2023.10161160 10.1609/aaai.v32i1.12301 10.48550/ARXIV.1706.03762 10.1109/CVPR.2016.90 10.1007/978-3-030-58589-1_40 10.1007/978-3-031-20047-2_9 10.1007/978-3-030-58586-0_17 10.1109/ICCV51070.2023.00730 10.1007/978-3-031-20077-9_1 10.1109/ICCV.2019.00301 10.1109/LRA.2020.3004325 10.1109/CVPR52729.2023.00103 10.1109/TITS.2019.2890870 10.1609/aaai.v35i4.16469 10.1109/CVPR46437.2021.00036 10.1109/LSP.2006.879465 10.1007/978-3-030-58452-8_13 |
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| References | ref13 ref12 ref15 ref14 ref20 Huang (ref22) ref11 ref10 ref21 ref2 ref1 ref17 ref18 ref8 Dosovitskiy (ref16) 2021 ref7 ref9 ref4 ref3 ref6 ref5 Tan (ref19) 2019 |
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| SubjectTerms | 3D Lane detection attention in attention attention mechanism Cameras Correlation Critical components Data mining Feature extraction feature fusion Lane detection Object recognition Three-dimensional displays Transformers |
| Title | 3D Lane Detection With Attention in Attention |
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