@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",
}