---
title: StreakNet-Models
canonical_url: "https://www.modelscope.cn/models/CoderAN/StreakNet-Models"
md_url: "https://www.modelscope.cn/models/CoderAN/StreakNet-Models.md"
repository: CoderAN/StreakNet-Models
last_updated: 2024-04-16
license: apache-2.0
library_name:
  - pytorch
frameworks:
  - Pytorch
downloads: 31
stars: 0
---

# StreakNet-Models

> StreakNet-Models - CoderAN 在 ModelScope 开源的模型。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…

CoderAN/StreakNet-Models 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可。

- **Repository**: CoderAN/StreakNet-Models
- **License**: apache-2.0
- **Downloads**: 31
- **Stars**: 0
- **Last updated**: 2024-04-16

Source: https://www.modelscope.cn/models/CoderAN/StreakNet-Models

---

<div align="center"><img src="./assets/streaknet_logo.png" width="400"></div><br>
<div align="center"><img src="./assets/overview.jpg"></div>

## Introduction

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.

For further details, please refer to our [paper](https://arxiv.org/abs/2404.09158).