Frequency–Spatial Joint Decoupling With Adaptive Perceptual Aggregation for Remote Sensing Small Object Detection

Abstract

Small object detection in remote sensing (RS) imagery remains fundamentally challenging due to severe information degradation caused by limited spatial resolution and complex background interference. In deep neural networks, such degradation is further exacerbated by irreversible information loss during conventional downsampling, leading to weak and ambiguous feature representations. To address this issue, we propose a frequency–spatial joint decoupling and adaptive perceptual aggregation module (FSD-APAM), which explicitly separates signal-level detail preservation and perceptual-level feature discrimination within a unified framework. Specifically, a frequency-domain detail decoupling (FDD) unit leverages discrete wavelet transform to construct reversible feature pathways, enabling the recovery of high-frequency edge information suppressed during downsampling. Complementarily, a spatial salience decoupling (SSD) unit introduces a lateral inhibition mechanism to enhance isolated target responses while suppressing structured background interference. To further ensure global contextual consistency with low computational overhead, an adaptive contextual perceptual aggregation (ACPA) unit is designed to facilitate efficient interaction between sparse target cues and dense semantic representations. Extensive experiments on AI-TODV2, LEVIR-Ship, and VisDrone demonstrate that the proposed method consistently improves detection performance across multiple mainstream architectures without requiring substantial architectural modifications. In particular, FSD-APAM achieves superior accuracy in detecting extremely small objects while maintaining competitive efficiency, highlighting its practical value for large-scale RS applications. The source code is available at https://github.com/cskkx1/FSD-APAM