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

2026-01-23

IEEE Geoscience and Remote Sensing Letters

AI Summary

This work addresses the challenges in infrared small target detection, where deep networks often cause edge degradation and conventional convolutions struggle to effectively disentangle frequency-domain features, leading to low-frequency background interference with high-frequency targets and consequent false alarms from noise. To mitigate these issues, the authors propose MDAFNet, which innovatively integrates a Multi-Scale Differential Edge (MSDE) module and a Dual-domain Adaptive Feature Enhancement (DAFE) module. The MSDE module compensates for edge information loss during downsampling, while the DAFE module mimics frequency-domain decomposition in the spatial domain, enabling synergistic optimization across spatial and frequency domains to differentially enhance high-frequency targets and suppress noise. Experimental results demonstrate that the proposed method significantly improves detection accuracy and robustness across multiple datasets.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Adversarial Attacks & Robustness
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Infrared small target detection (IRSTD) plays a crucial role in numerous military and civilian applications. However, the 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 multiscale differential edge and adaptive frequency guided network for IRSTD (MDAFNet), which integrates the multiscale differential edge (MSDE) module and dual-domain adaptive feature enhancement (DAFE) module. The MSDE module, through a multiscale 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.

Problem

• Infrared small target detection
• edge degradation
• frequency interference
• false detection
• feature extraction

Innovation

• Multiscale Differential Edge
• Adaptive Frequency Guidance
• Infrared Small Target Detection
• Dual-Domain Feature Enhancement
• Frequency-aware Convolution