轻量级网络RSNet提升复杂背景中雷达船检出率
RSNet: A Light Framework for The Detection of SAR Ship Detection
- 用小波特征引导全局上下文，减少参数同时保持精度
- 在SSDD和HRSID数据集上分别达72.5%和67.6% mAP
- 适合资源受限场景下的实时舰船检测应用
基于深度学习的合成孔径雷达（SAR）舰船检测虽显著提升了准确率与速度，但在复杂背景下小目标检测且参数量少仍是难题。本文提出轻量级框架RSNet，其主干采用多尺度小波特征引导的Waveletpool-ContextGuided（WCG）结构，增强复杂场景下的全局理解能力；颈部引入残差小波逐元素相乘的Waveletpool-StarFusion（WSF），在不增加网络宽度的前提下生成高维非线性特征；检测头设计轻量共享卷积模块（LS），实现高效检测与多格式兼容。在SAR Ship Detection Dataset（SSDD）和High-Resolution SAR Image Dataset（HRSID）上的实验表明，RSNet仅用1.49M参数即达到72.5%和67.6%的mAP_{.50:.95}，优于多数先进检测器。
原文摘要 · Abstract (English)
Recent advancements in synthetic aperture radar (SAR) ship detection using deep learning have significantly improved accuracy and speed, yet effectively detecting small objects in complex backgrounds with fewer parameters remains a challenge. This letter introduces RSNet, a lightweight framework constructed to enhance ship detection in SAR imagery. To ensure accuracy with fewer parameters, we proposed Waveletpool-ContextGuided (WCG) as its backbone, guiding global context understanding through multi-scale wavelet features for effective detection in complex scenes. Additionally, Waveletpool-StarFusion (WSF) is introduced as the neck, employing a residual wavelet element-wise multiplication structure to achieve higher dimensional nonlinear features without increasing network width. The Lightweight-Shared (LS) module is designed as detect components to achieve efficient detection through lightweight shared convolutional structure and multi-format compatibility. Experiments on the SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID) demonstrate that RSNet achieves a strong balance between lightweight design and detection performance, surpassing many state-of-the-art detectors, reaching 72.5\% and 67.6\% in \textbf{\(\mathbf{mAP_{.50:.95}}\) }respectively with 1.49M parameters. Our code will be released soon.
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