StreakNet-Arch: An Anti-scattering Network-based Architecture for Underwater Carrier LiDAR-Radar Imaging

TL;DR

StreakNet-Arch is a real-time underwater imaging architecture that uses self-attention mechanisms to improve scatter suppression in LiDAR-Radar systems, validated through controlled experiments, sea trials, and a new dataset.

Contribution

The paper introduces StreakNet-Arch, a novel real-time binary classification framework with self-attention and DBC-Attention for underwater imaging, outperforming traditional methods.

Findings

Outperforms traditional bandpass filtering and learning-based networks in controlled tests.

Achieves real-time processing with consistent imaging times on NVIDIA RTX 3060.

Validates system effectiveness with a 46mm error at 1,000m depth in sea trials.

Abstract

In this paper, we introduce StreakNet-Arch, a real-time, end-to-end binary-classification framework based on our self-developed Underwater Carrier LiDAR-Radar (UCLR) that embeds Self-Attention and our novel Double Branch Cross Attention (DBC-Attention) to enhance scatter suppression. Under controlled water tank validation conditions, StreakNet-Arch with Self-Attention or DBC-Attention outperforms traditional bandpass filtering and achieves higher $F_1$ scores than learning-based MP networks and CNNs at comparable model size and complexity. Real-time benchmarks on an NVIDIA RTX 3060 show a constant Average Imaging Time (54 to 84 ms) regardless of frame count, versus a linear increase (58 to 1,257 ms) for conventional methods. To facilitate further research, we contribute a publicly available streak-tube camera image dataset contains 2,695,168 real-world underwater 3D point cloud data. More importantly, we validate our UCLR system in a South China Sea trial, reaching an error of 46mm for 3D target at 1,000 m depth and 20 m range. Source code and data are available at https://github.com/BestAnHongjun/StreakNet .