StreakNet-Arch: An Anti-scattering Network-based Architecture for Underwater Carrier LiDAR-Radar Imaging
文献类型：预印本
| 作者 | Li, Xuelong2,3 ; An, Hongjun3; Li, Guangying1; Wang, Xing1; Cheng, Guanghua3; Sun, Zhe3
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| 英文摘要 | In this paper, we introduce StreakNet-Arch, a novel signal processing architecture designed for Underwater Carrier LiDAR-Radar (UCLR) imaging systems, to address the limitations in scatter suppression and real-time imaging. StreakNet-Arch formulates the signal processing as a real-time, end-to-end binary classification task, enabling real-time image acquisition. To achieve this, we leverage Self-Attention networks and propose a novel Double Branch Cross Attention (DBC-Attention) mechanism that surpasses the performance of traditional methods. Furthermore, we present a method for embedding streak-tube camera images into attention networks, effectively acting as a learned bandpass filter. To facilitate further research, we contribute a publicly available streak-tube camera image dataset. The dataset contains 2,695,168 real-world underwater 3D point cloud data. These advancements significantly improve UCLR capabilities, enhancing its performance and applicability in underwater imaging tasks. The source code and dataset can be found at https://github.com/BestAnHongjun/StreakNet. Copyright © 2024, The Authors. All rights reserved. |
| 出处 | arXiv |
| 出版者 | arXiv |
| ISSN号 | 23318422 |
| 发表日期 | 2024-04-14 |
| 语种 | 英语 |
| 产权排序 | 3 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/97474]  |
| 专题 | 条纹相机工程中心 |
| 作者单位 | 1.State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics of CAS, Shaanxi, Xi’an; 710119, China 2.The Institute of Artificial Intelligence (TeleAI), China Telecom Corp Ltd, Beijing; 100033, China; 3.School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Shaanxi, Xi’an; 710072, China; |
| 推荐引用方式 GB/T 7714 | Li, Xuelong,An, Hongjun,Li, Guangying,et al. StreakNet-Arch: An Anti-scattering Network-based Architecture for Underwater Carrier LiDAR-Radar Imaging. 2024. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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