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StreakNet-Arch: An Anti-scattering Network-based Architecture for Underwater Carrier LiDAR-Radar Imaging
Li, Xuelong1,2; An, Hongjun1; Li, Guangying3; Wang, Xing3; Cheng, Guanghua1; Sun, Zhe1
作者部门条纹相机工程中心
2024-04-14
出处arXiv
ISSN23318422
产权排序3
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.
收录类别EI
语种英语
出版者arXiv
EI入藏号20240190185
文献类型预印本
条目标识符http://ir.opt.ac.cn/handle/181661/97474
专题条纹相机工程中心
作者单位1.School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Shaanxi, Xi’an; 710072, China;
2.The Institute of Artificial Intelligence (TeleAI), China Telecom Corp Ltd, Beijing; 100033, China;
3.State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics of CAS, Shaanxi, Xi’an; 710119, 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.
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