ABMNet: Coupling Transformer with CNN Based on Adams-Bashforth-Moulton Method for Infrared Small Target Detection

Infrared small target detection (ISTD) aims at segmenting the small targets from infrared images, which has wide applications in military surveillance. Present methods are mainly based on CNN and focus on modelling locality while ignoring global dependencies, which are indispensable because the local areas similar to small targets always spread over most of the background, causing heavy target ambiguity. Recently, RKformer [1] has combined local features with global dependencies and further introduced Runge-Kutta method, a one-step Ordinary Differential Equation (ODE) solver, to ISTD and performed well. However, the method simply fuses features from original transformer and residual blocks by naive concatenation, causing insufficient feature interaction. Also, it inevitably brings effective information loss, which greatly impairs ambiguous target features. To address above problems and target ambiguity, we introduce Adams-Bashforth-Moulton method and propose ABMNet, which has (1) multi-step memory and self-rectification mechanisms, guaranteeing more sufficient information usage and more accurate detection, (2) and achieves more sufficient interaction of both local and global information. Experiments on MDFA and IRSTD-1k demonstrate the superiority of our method.