CCLane: Concise Curve Anchor-Based Lane Detection Model with MLP-Mixer

Fan Yang; Yanan Zhao; Li Gao; Huachun Tan; Weijin Liu; Xue mei Chen; Shijuan Yang

doi:10.1007/978-981-99-8435-0_30

CCLane: Concise Curve Anchor-Based Lane Detection Model with MLP-Mixer

Fan Yang, Yanan Zhao^*, Li Gao, Huachun Tan, Weijin Liu, Xue mei Chen, Shijuan Yang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Lane detection needs to meet the real-time requirements and efficiently utilize both local and global information on the feature map. In this paper, we propose a new lane detection model called CCLane, which uses the pre-set curve anchor method to better utilize the prior information of the lane. Based on the Cross Layer Refinement method for extracting local information at different levels, we propose a way to combine MLP-Mixer and spatial convolution to obtain global information and achieve information transmission between lanes, which flexibly and efficiently integrates local and global information. We also extend the DIoU loss function to lane detection and design the LDIoU loss function. The method is evaluated on two widely used lane detection datasets, and the results show that our method performs well.

Original language	English
Title of host publication	Pattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings
Editors	Qingshan Liu, Hanzi Wang, Rongrong Ji, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	376-387
Number of pages	12
ISBN (Print)	9789819984343
DOIs	https://doi.org/10.1007/978-981-99-8435-0_30
Publication status	Published - 2024
Event	6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023 - Xiamen, China Duration: 13 Oct 2023 → 15 Oct 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14427 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023
Country/Territory	China
City	Xiamen
Period	13/10/23 → 15/10/23

Keywords

Lane detection
Line anchor
MLP

Access to Document

10.1007/978-981-99-8435-0_30

Cite this

Yang, F., Zhao, Y., Gao, L., Tan, H., Liu, W., Chen, X. M., & Yang, S. (2024). CCLane: Concise Curve Anchor-Based Lane Detection Model with MLP-Mixer. In Q. Liu, H. Wang, R. Ji, Z. Ma, W. Zheng, H. Zha, X. Chen, & L. Wang (Eds.), Pattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings (pp. 376-387). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14427 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-8435-0_30

Yang, Fan ; Zhao, Yanan ; Gao, Li et al. / CCLane : Concise Curve Anchor-Based Lane Detection Model with MLP-Mixer. Pattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings. editor / Qingshan Liu ; Hanzi Wang ; Rongrong Ji ; Zhanyu Ma ; Weishi Zheng ; Hongbin Zha ; Xilin Chen ; Liang Wang. Springer Science and Business Media Deutschland GmbH, 2024. pp. 376-387 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{25f731c654af4338af23c80ce24455a4,

title = "CCLane: Concise Curve Anchor-Based Lane Detection Model with MLP-Mixer",

abstract = "Lane detection needs to meet the real-time requirements and efficiently utilize both local and global information on the feature map. In this paper, we propose a new lane detection model called CCLane, which uses the pre-set curve anchor method to better utilize the prior information of the lane. Based on the Cross Layer Refinement method for extracting local information at different levels, we propose a way to combine MLP-Mixer and spatial convolution to obtain global information and achieve information transmission between lanes, which flexibly and efficiently integrates local and global information. We also extend the DIoU loss function to lane detection and design the LDIoU loss function. The method is evaluated on two widely used lane detection datasets, and the results show that our method performs well.",

keywords = "Lane detection, Line anchor, MLP",

author = "Fan Yang and Yanan Zhao and Li Gao and Huachun Tan and Weijin Liu and Chen, {Xue mei} and Shijuan Yang",

note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023 ; Conference date: 13-10-2023 Through 15-10-2023",

year = "2024",

doi = "10.1007/978-981-99-8435-0_30",

language = "English",

isbn = "9789819984343",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "376--387",

editor = "Qingshan Liu and Hanzi Wang and Rongrong Ji and Zhanyu Ma and Weishi Zheng and Hongbin Zha and Xilin Chen and Liang Wang",

booktitle = "Pattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings",

address = "Germany",

}

Yang, F, Zhao, Y, Gao, L, Tan, H, Liu, W, Chen, XM & Yang, S 2024, CCLane: Concise Curve Anchor-Based Lane Detection Model with MLP-Mixer. in Q Liu, H Wang, R Ji, Z Ma, W Zheng, H Zha, X Chen & L Wang (eds), Pattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14427 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 376-387, 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, Xiamen, China, 13/10/23. https://doi.org/10.1007/978-981-99-8435-0_30

CCLane: Concise Curve Anchor-Based Lane Detection Model with MLP-Mixer. / Yang, Fan; Zhao, Yanan; Gao, Li et al.
Pattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings. ed. / Qingshan Liu; Hanzi Wang; Rongrong Ji; Zhanyu Ma; Weishi Zheng; Hongbin Zha; Xilin Chen; Liang Wang. Springer Science and Business Media Deutschland GmbH, 2024. p. 376-387 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14427 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - CCLane

T2 - 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023

AU - Yang, Fan

AU - Zhao, Yanan

AU - Gao, Li

AU - Tan, Huachun

AU - Liu, Weijin

AU - Chen, Xue mei

AU - Yang, Shijuan

PY - 2024

Y1 - 2024

N2 - Lane detection needs to meet the real-time requirements and efficiently utilize both local and global information on the feature map. In this paper, we propose a new lane detection model called CCLane, which uses the pre-set curve anchor method to better utilize the prior information of the lane. Based on the Cross Layer Refinement method for extracting local information at different levels, we propose a way to combine MLP-Mixer and spatial convolution to obtain global information and achieve information transmission between lanes, which flexibly and efficiently integrates local and global information. We also extend the DIoU loss function to lane detection and design the LDIoU loss function. The method is evaluated on two widely used lane detection datasets, and the results show that our method performs well.

AB - Lane detection needs to meet the real-time requirements and efficiently utilize both local and global information on the feature map. In this paper, we propose a new lane detection model called CCLane, which uses the pre-set curve anchor method to better utilize the prior information of the lane. Based on the Cross Layer Refinement method for extracting local information at different levels, we propose a way to combine MLP-Mixer and spatial convolution to obtain global information and achieve information transmission between lanes, which flexibly and efficiently integrates local and global information. We also extend the DIoU loss function to lane detection and design the LDIoU loss function. The method is evaluated on two widely used lane detection datasets, and the results show that our method performs well.

KW - Lane detection

KW - Line anchor

KW - MLP

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DO - 10.1007/978-981-99-8435-0_30

M3 - Conference contribution

AN - SCOPUS:85180807873

SN - 9789819984343

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 376

EP - 387

BT - Pattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings

A2 - Liu, Qingshan

A2 - Wang, Hanzi

A2 - Ji, Rongrong

A2 - Ma, Zhanyu

A2 - Zheng, Weishi

A2 - Zha, Hongbin

A2 - Chen, Xilin

A2 - Wang, Liang

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 13 October 2023 through 15 October 2023

ER -

Yang F, Zhao Y, Gao L, Tan H, Liu W, Chen XM et al. CCLane: Concise Curve Anchor-Based Lane Detection Model with MLP-Mixer. In Liu Q, Wang H, Ji R, Ma Z, Zheng W, Zha H, Chen X, Wang L, editors, Pattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 376-387. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-981-99-8435-0_30