Enhanced Cross-Domain Dim and Small Infrared Target Detection via Content-Decoupled Feature Alignment

The detection and recognition of dim and small infrared (IR) targets across domains pose two formidable challenges: distributional discrepancies in samples and scarcity or absence of annotated instances in the target domain. While current unsupervised domain adaptive object detection (DAOD) methods can somewhat alleviate the performance degradation caused by these issues, they fail to address the differences in semantic content between the background environments in different application scenarios. This results in a semantic gap that impedes the algorithm’s adaptability. We propose a content-decoupled unsupervised domain adaptive method to mitigate the adverse impact of the semantic gap on domain adaptation. Specifically, we introduce an unsupervised task-guided branch in the one-stage detector that executes style transfer in real time during training, directing the feature representation via shared parameters. A novel content-decoupled feature alignment module aligns semantically unrelated features in the source and target domains while preserving the intrinsic semantics of dim and small IR targets. The experiments on a cross-band IR dataset reveal that the proposed method enhances the mean average precision (AP) by 18.3% compared to the baseline detector without increasing computational burden.