Denoising-Enhanced Coarse-to-Fine Infrared Small Target Detection with Attention Prior-Guided Knowledge Distillation

Infrared small target detection (IRSTD) in high-resolutionimages is crucial for many practical applications, such as surveillanceof unmanned aerial vehicles (UAVs) and UAV-based ground monitoring.However, IRSTD remains challenging due to the small size and weakfeatures of targets, as well as significant interference from complex dy-namic backgrounds. Existing detection methods often suffer from redun-dant computations on non-target background regions and insufficient ex-ploitation of target context information, which limits their performancein complex backgrounds. To address these issues, we propose an efficientcoarse-to-fine infrared small target detection framework with attentionprior-guided knowledge distillation, termed ECFNet. In the coarse stage,we design a region binary classification network (RBCN) on grid-basedmulti-scale feature maps to efficiently recognize target-containing con-text region proposals. Moreover, we introduce a novel denoising-assistedtraining strategy that incorporates noisy ground-truth (GT) masks intoRBCN feature maps. By training the network to reconstruct the GTmasks via a denoising task, this strategy encourages the model to ex-plicitly capture target-background context, thereby enhancing its abil-ity to distinguish target proposals from background regions. In the finestage, we customize a lightweight target detector to the coarse stage’sregion proposals for balancing accuracy and efficiency. Furthermore, wepropose a knowledge distillation strategy guided by the teacher-studentcross-attention prior. This mechanism directs the student to focus oncritical target regions, thereby enhancing the discriminative feature rep-resentation for infrared small targets. Extensive experiments on threereal infrared datasets demonstrate that our method outperforms bothexisting single-stage and two-stage approaches while maintaining highreal-time processing efficiency.