DEFANet: Dual-Path Edge-Target Collaboration with Frequency-Aware Enhancement for Infrared Small Target Detection

Abstract

Infrared small target detection is challenging due to limited target size and low signal-to-noise ratio. Unlike common targets, infrared small targets contain a higher proportion of edge pixels and exhibit blurred boundaries due to diffraction and quantization artifacts, making boundaries uniquely valuable cues for target perception. However, existing methods often emphasize holistic modeling while underutilizing such informative boundary cues. Motivated by this observation, we propose a Dual-Path Edge-Guided Frequency-Aware Network (DEFANet), which enables edge-target collaborative modeling for enhanced feature representation. DEFANet features a dual-path design, consisting of a main branch for holistic target modeling and an edge branch for boundary transition perception. To facilitate interaction and enhance representation in both branches, we introduce two core modules: Frequency-Aware Dual Enhancement Module (FADE) and Edge-Guided Integration Module (EGI). FADE employs a Frequency-Decoupled Attention Enhancement Mechanism to enhance both branches in the frequency domain, strengthening holistic modeling in the main branch and boundary representation in the edge branch. EGI leverages a Dual-Path Group-Wise Guidance Mechanism to integrate enhanced edge features into the main branch, improving boundary perception. Extensive experiments on four public infrared small target datasets, MDvsFA, LAFT, SIRST, and SIATD, demonstrate that DEFANet achieves SOTA performance. Ablation studies further validate the effectiveness of DEFANet and the soundness of its design motivation.