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.

翻译：红外小目标检测在众多军事和民用应用中发挥着至关重要的作用。然而，现有方法常面临随着网络层数增加，目标边缘像素逐渐退化的问题，且传统卷积在特征提取过程中难以区分频率分量，导致低频背景干扰高频目标，高频噪声引发误检。为应对这些局限，我们提出MDAFNet（用于红外小目标检测的多尺度差分边缘与自适应频率引导网络），该网络集成了多尺度差分边缘模块与双域自适应特征增强模块。MSDE模块通过多尺度边缘提取与增强机制，有效补偿了下采样过程中目标边缘信息的累积损失。DAFE模块将频域处理机制与空间域中的模拟频率分解及融合机制相结合，显著提升了网络自适应增强高频目标与选择性抑制高频噪声的能力。在多个数据集上的实验结果表明，MDAFNet具有优越的检测性能。