Lane-DeepLab: Lane semantic segmentation in automatic driving scenarios for high-definition maps
文献类型：期刊论文
| 作者 | Li, Jingyu1,2; Jiang, Fengling1,2,4; Yang, Jing1,3; Kong, Bin1,3 ; Gogate, Mandar5; Dashtipour, Kia5; Hussain, Amir5
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| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2021-11-20 |
| 卷号 | 465 |
| 关键词 | Lane detection Semantic segmentation High-definition maps Attention mechanism |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2021.08.105 |
| 通讯作者 | Kong, Bin(bkong@iim.ac.cn) |
| 英文摘要 | Accurate high-definition maps with lane markings are often used as the navigation back-end for commercial autonomous vehicles. Currently, most high-definition maps are manually constructed by human labelling. Therefore, it is urgently required to propose a multi-class lane detection method that can automatically mark the road lanes to assist in generating high-precision maps for autonomous driving. We propose a lane segmentation detection method, named Lane-DeepLab, which is based on semantic segmentation for detecting multi-class lane lines in unmanned driving scenarios. The proposed method is based on the DeepLabv3+ network as the baseline, and we have redesigned the encoder-decoder structure to generate more accurate lane line detection results. More specifically, we restructure the atrous convolution at multi-scale by applying attention mechanism. Subsequently, we employ the Semantic Embedding Branch (SEB) to combine the high-level and low-level semantic information to obtain more abundant features, and use the Single Stage Headless (SSH) context module to obtain multi-scale information. Finally, we fuse the results to generate automatic high-precision mapping results. Our method has improved performance compared with other methods in the ApolloScape part of the dataset. Besides, in the database of Cityscapes, our approach has also achieved good results in semantic segmentation. Experimental results demonstrate that our proposed Lane-DeepLab can provide excellent performance in real traffic scenarios. (c) 2021 Elsevier B.V. All rights reserved. |
| 资助项目 | National Natural Science Foundation of China[91320301] ; Technological Innovation Project for New Energy and Intelligent Networked Automobile Industry of Anhui Province ; Innovation Research Institute of Robotics and Intelligent Manufacturing(CAS) ; Natural Science Foundation of Education Bureau of Anhui Province[KJ2020A0111] ; Anhui Provincial Key Laboratory of Multimodal Cognitive Computation[MMC202007] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000696929800002 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; Technological Innovation Project for New Energy and Intelligent Networked Automobile Industry of Anhui Province ; Innovation Research Institute of Robotics and Intelligent Manufacturing(CAS) ; Natural Science Foundation of Education Bureau of Anhui Province ; Anhui Provincial Key Laboratory of Multimodal Cognitive Computation |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/124949]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Kong, Bin |
| 作者单位 | 1.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China 3.Anhui Engn Lab Intelligent Driving Technol & Appl, Hefei 230088, Peoples R China 4.Hefei Normal Univ, Hefei 230061, Peoples R China 5.Edinburgh Napier Univ, Sch Comp, Merchiston Campus, Edinburgh EH10 5DT, Midlothian, Scotland |
| 推荐引用方式 GB/T 7714 | Li, Jingyu,Jiang, Fengling,Yang, Jing,et al. Lane-DeepLab: Lane semantic segmentation in automatic driving scenarios for high-definition maps[J]. NEUROCOMPUTING,2021,465. |
| APA | Li, Jingyu.,Jiang, Fengling.,Yang, Jing.,Kong, Bin.,Gogate, Mandar.,...&Hussain, Amir.(2021).Lane-DeepLab: Lane semantic segmentation in automatic driving scenarios for high-definition maps.NEUROCOMPUTING,465. |
| MLA | Li, Jingyu,et al."Lane-DeepLab: Lane semantic segmentation in automatic driving scenarios for high-definition maps".NEUROCOMPUTING 465(2021). |
入库方式： OAI收割
来源：合肥物质科学研究院
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