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 .

翻译：本文提出了一种面向水下载波LiDAR-Radar（UCLR）成像系统的新型信号处理架构StreakNet-Arch，旨在解决散射抑制与实时成像方面的局限性。该架构将信号处理建模为实时端到端二分类任务，从而实现了实时图像采集。为此，我们利用自注意力网络，并提出了双支路交叉注意力（DBC-Attention）机制，其性能超越了传统方法。此外，我们提出了一种将条纹管相机图像嵌入注意力网络的方法，该方法可有效充当可学习的带通滤波器。为促进后续研究，我们贡献了一个公开可用的条纹管相机图像数据集，包含2,695,168组真实水下三维点云数据。这些进展显著提升了UCLR的能力，增强了其在水下成像任务中的性能与适用性。源代码与数据集获取地址：https://github.com/BestAnHongjun/StreakNet。