提出新网络提升红外小目标检测精度，解决边缘丢失和噪声干扰问题。
MDAFNet: Multiscale Differential Edge and Adaptive Frequency Guided Network for Infrared Small Target Detection
- 设计多尺度边缘增强模块，缓解深层网络中目标边缘信息损失。
- 引入自适应频域增强机制，有效区分目标与背景噪声频率成分。
- 在多个数据集上表现领先，适合军事与安防领域的小目标检测任务。
红外小目标检测（IRSTD）在军事和民用领域具有重要意义。然而，现有方法常因网络层数增加导致目标边缘像素逐渐退化，传统卷积难以区分特征提取中的频率成分，致使低频背景干扰高频目标，高频噪声引发误检。为此，本文提出MDAFNet（多尺度差异边缘与自适应频率引导网络），融合多尺度差异边缘（MSDE）模块与双域自适应特征增强（DAFE）模块。MSDE模块通过多尺度边缘提取与增强机制，有效补偿下采样过程中的目标边缘信息累积损失。DAFE模块结合频域处理与空间域模拟频率分解融合机制，显著提升网络对高频目标的自适应增强能力，并选择性抑制高频噪声。在多个数据集上的实验结果表明，MDAFNet具备优越的检测性能。
原文摘要 · Abstract (English)
Infrared small target detection (IRSTD) plays a crucial role in numerous military and civilian applications. However, existing methods often face the gradual degradation of target edge pixels as the number of network layers increases, and traditional convolution struggles to differentiate between frequency components during feature extraction, leading to low-frequency backgrounds interfering with high-frequency targets and high-frequency noise triggering false detections. To address these limitations, we propose MDAFNet (Multi-scale Differential Edge and Adaptive Frequency Guided Network for Infrared Small Target Detection), which integrates the Multi-Scale Differential Edge (MSDE) module and Dual-Domain Adaptive Feature Enhancement (DAFE) module. The MSDE module, through a multi-scale edge extraction and enhancement mechanism, effectively compensates for the cumulative loss of target edge information during downsampling. The DAFE module combines frequency domain processing mechanisms with simulated frequency decomposition and fusion mechanisms in the spatial domain to effectively improve the network's capability to adaptively enhance high-frequency targets and selectively suppress high-frequency noise. Experimental results on multiple datasets demonstrate the superior detection performance of MDAFNet.
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