用Mamba结构生成目标框，提升复杂背景下的雷达目标检测精度
MaDiNet: Mamba Diffusion Network for SAR Target Detection
- 将雷达目标检测转为生成边界框位置与尺寸的任务
- 在多个数据集上达到当前最优性能，显著抑制背景干扰
- 适合需要高鲁棒性雷达目标检测的军事与遥感应用
合成孔径雷达（SAR）目标检测的核心挑战在于如何在非合作环境下构建具有区分性、高效且鲁棒的目标表征。然而，目标稀疏分布、离散特征以及复杂背景干扰严重影响了检测精度。本文提出一种基于Mamba与扩散机制的网络架构MaDiNet，将目标检测视为在图像空间中生成目标边界框中心坐标及宽高。设计MambaSAR模块以捕捉目标复杂的空间结构信息，增强模型对目标与复杂背景的区分能力。在多个SAR目标检测数据集上的实验结果表明，该方法达到当前最优性能，验证了其有效性。代码已开源。
原文摘要 · Abstract (English)
The fundamental challenge in SAR target detection lies in developing discriminative, efficient, and robust representations of target characteristics within intricate non-cooperative environments. However, accurate target detection is impeded by factors including the sparse distribution and discrete features of the targets, as well as complex background interference. In this study, we propose a \textbf{Ma}mba \textbf{Di}ffusion \textbf{Net}work (MaDiNet) for SAR target detection. Specifically, MaDiNet conceptualizes SAR target detection as the task of generating the position (center coordinates) and size (width and height) of the bounding boxes in the image space. Furthermore, we design a MambaSAR module to capture intricate spatial structural information of targets and enhance the capability of the model to differentiate between targets and complex backgrounds. The experimental results on extensive SAR target detection datasets achieve SOTA, proving the effectiveness of the proposed network. Code is available at \href{https://github.com/JoyeZLearning/MaDiNet}{https://github.com/JoyeZLearning/MaDiNet}.
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