ParallelEye Pipeline: An Effective Method to Synthesize Images for Improving the Visual Intelligence of Intelligent Vehicles
文献类型：期刊论文
| 作者 | Li, Xuan1; Wang, Kunfeng2 ; Gu, Xianfeng3; Deng, Fang4; Wang, Fei-Yue5
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| 刊名 | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
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| 出版日期 | 2023-05-16 |
| 页码 | 12 |
| ISSN号 | 2168-2216 |
| 关键词 | Annotations Pipelines Autonomous vehicles Generative adversarial networks Task analysis Semantics Visualization Generative adversarial network (GAN) intelligent vehicles object detection simulated scene synthetic image |
| DOI | 10.1109/TSMC.2023.3273896 |
| 通讯作者 | Wang, Kunfeng(wangkf@mail.buct.edu.cn) |
| 英文摘要 | Virtual simulated scenes are becoming a critical part of autonomous driving. In the context of knowledge automation and machine learning, simulated images are widely used for visual environmental perception. However, even the most inspirational applications have not fully exploited the potential of simulated images in solving real-world problems. In this article, we propose a novel framework "ParallelEye Pipeline", which uses image-to-image translation and simulated images to automatically generate realistic synthetic images with multiple ground-truth annotations. Specifically, this method has three steps: first, we use Unity3D software to simulate driving scenarios and generate simulated image pairs (including raw images and six ground-truth labels) from the simulated scenes; second, advanced image-to-image translation algorithms can generate realistic and high-resolution synthetic images from simulated image pairs; third, we exploit publicly datasets, simulated images, and synthetic images to conduct experiments for visual perception. The experimental results suggest: 1) synthetic images and simulated images can improve the performance of detectors in real autonomous driving scenarios and 2) image-to-image translation algorithms can be affected by occlusion condition. |
| WOS关键词 | VISION |
| 资助项目 | National Key Research and Development Program of China[2020YFC2003900] ; National Natural Science Foundation of China[62076020] ; National Natural Science Foundation of China[62203250] ; Young Elite Scientists Sponsorship Program of China Association of Science and Technology[YESS20210289] ; China Postdoctoral Science Foundation[2020TQ1057] ; China Postdoctoral Science Foundation[2020M682823] ; China Scholarship Council |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001005366200001 |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program of China Association of Science and Technology ; China Postdoctoral Science Foundation ; China Scholarship Council |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53504]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Wang, Kunfeng |
| 作者单位 | 1.Peng Cheng Lab, Dept Math & Theories, Shenzhen 518000, Peoples R China 2.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China 3.SUNY Stony Brook, Dept Comp Sci, Stony Brook, NY 11794 USA 4.Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China 5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Xuan,Wang, Kunfeng,Gu, Xianfeng,et al. ParallelEye Pipeline: An Effective Method to Synthesize Images for Improving the Visual Intelligence of Intelligent Vehicles[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2023:12. |
| APA | Li, Xuan,Wang, Kunfeng,Gu, Xianfeng,Deng, Fang,&Wang, Fei-Yue.(2023).ParallelEye Pipeline: An Effective Method to Synthesize Images for Improving the Visual Intelligence of Intelligent Vehicles.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,12. |
| MLA | Li, Xuan,et al."ParallelEye Pipeline: An Effective Method to Synthesize Images for Improving the Visual Intelligence of Intelligent Vehicles".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2023):12. |
入库方式： OAI收割
来源：自动化研究所
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