SMN: Signal Modulation Network for Tiny Object Detection in Remote Sensing Imagery

Abstract

Tiny object detection (TOD) in remote sensing imagery remains challenging because foreground signals are extremely weak in deep feature hierarchies and are easily overwhelmed by high-response background interference. To mitigate this observed foreground-background signal modulation imbalance (FBSMI) difficulty, we propose a signal modulation network (SMN) for remote-sensing TOD. SMN comprises two complementary components. First, an adaptive Wiener filter modulator (AWFM) is inserted after backbone stages to suppress background-dominated noise while preserving weak target-related responses at multiple resolutions. Second, we introduce the novel denoising diffusion transformer (DDT), a feature-space conditional diffusion module that operates on detector feature tensors rather than image pixels. DDT generates multiple diffusion-guided semantic feature variants from high-level fused features and expands the local representation space around weak tiny object evidence. Extensive experiments on AI-TOD, SODA-A, DOTAv2.0, and DIOR-R demonstrate that SMN not only effectively mitigates the FBSMI problem, but also improves detection accuracy, particularly for very tiny and tiny objects, compared with state-of-the-art methods.