An Asymmetric Intensive Interactive Fusion Network for Infrared Small Target Detection

The accurate and stable detection of infrared (IR) small targets is essential for long-range monitoring. However, current approaches struggle to effectively bridge the inherent semantic gaps between hierarchical features and emphasize their critical characteristics during multilevel feature fusion. To address this challenge, a new asymmetric intensive interactive fusion network (A<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {I}^{2}$ </tex-math></inline-formula>Net) is proposed to narrow the semantic gap and enhance feature refinement capability in multilevel feature fusion. A<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {I}^{2}$ </tex-math></inline-formula>Net employs a high-resolution network (HRNet) as its backbone, maintaining small target integrity in the network’s deeper layers through a high-resolution feature stream that preserves the information of raw small target. Furthermore, an asymmetric intensive interactive fusion (A<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {I}^{2}$ </tex-math></inline-formula>F) module based on differentiated asymmetric attention weight allocation is proposed to facilitate the interactive fusion of multilevel features and eliminate the semantic gap between hierarchical levels. After that, we develop a multiscale context feature extraction (MCFE) module to enrich spatial detail feature representation across multiple scales. The experimental results on the National University of Defense Technology Single-Frame InfraRed Small Target (NUDT-SIRST) Automatic Target Recognition key laboratory ground/air dataset (ATR ground/air dataset) demonstrate that A<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {I}^{2}$ </tex-math></inline-formula>Net achieves remarkable performance compared to the state-of-the-art methods.