Spectra-Net: Frequency-Aware Scale Adaptation for Small Object Detection

Abstract

Small object detection in real-world scenarios remains challenging due to two coupled factors: severe scale imbalance and progressive degradation of fine-grained cues during backbone downsampling. Under drastic scale variations, conventional detectors still rely on static backbones whose fixed convolutional responses cannot consistently accommodate the divergent spectral characteristics of large and tiny objects, leading to scale-mismatched representations. Meanwhile, repeated strided operations reduce the sampling rate of feature maps and tend to introduce aliasing, eroding the high-frequency details that are critical for tiny objects. To address these issues, we propose Spectra-Net, a novel frequency-aware detection framework that redesigns backbone feature encoding with explicit spectral control. At its core, we introduce Dynamic Fourier Alignment (DFA), which performs content-adaptive yet frequency-controllable modulation to reshape convolutional responses in the spectral domain, aligning representations across scales and amplifying discriminative cues for small objects. In addition, we develop Wavelet-Guided Spectral Downsampling (WGSD), which conducts explicit sub-band decomposition via Haar wavelets to suppress aliasing while selectively preserving informative high-frequency components during resolution reduction. Extensive experiments on VisDrone-2019 and TT100K, together with comprehensive ablations, demonstrate that Spectra-Net consistently improves small object detection performance under severe scale imbalance.