MDAFNet: Multiscale Differential Edge and Adaptive Frequency Guided Network for Infrared Small Target Detection

TL;DR

MDAFNet is a novel neural network designed for infrared small target detection, effectively enhancing target edges and suppressing noise through multi-scale and frequency domain mechanisms, leading to improved detection accuracy.

Contribution

The paper introduces MDAFNet, integrating MSDE and DAFE modules to address edge degradation and frequency interference issues in IRSTD, which is a novel approach in this field.

Findings

Superior detection performance on multiple datasets

Effective edge preservation during downsampling

Enhanced noise suppression through frequency domain processing

Abstract

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.