FLD-RTDETR: A Frequency-Aware Lightweight Network for Small-Object Detection in Aerial Imagery
Yu Tong, Kaina Xiong, Jun Liu*, Xinyue Fan, Guixing Cao
College of Computer Science and Engineering, Northeastern University, Shenyang, China
* Corresponding Author: Jun Liu. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.086756
Received 04 June 2026; Accepted 24 August 2026; Published online 10 September 2026
Abstract
Small-object detection in aerial remote-sensing imagery is constrained by the cascaded low-pass behaviour induced by consecutive spatial down-sampling, which progressively attenuates the high-frequency cues of weak targets. Recent detection transformers, whose self-attention operators behave—under commonly used temperature and rank regimes—as adaptive spatial low-pass filters, tend to inherit rather than mitigate this spectral bias, while existing remedies are typically obtained at the cost of considerable parameter and floating-point overhead. To bridge this gap, this paper proposes a Frequency-Aware Lightweight Network for Small-Object Detection (FLD-RTDETR), built upon the Real-Time Detection Transformer (RT-DETR), which augments this baseline along three orthogonal axes. A Partial Channel Bottleneck (PCB) routes a fraction of channels through identity bypass to preserve un-attenuated high-frequency channels while reducing redundant floating-point operations. A Local Spatial and Global Channel Attention (LSGCA) module factorizes self-attention into orthogonal spatial-covariance and channel-covariance branches, lowering complexity to local linearity. A Lightweight Multi-scale Fusion Neck (LMFN), centred on an Omni-Dimensional Frequency–Spatial Attention (ODFA) hub, injects explicit frequency-domain compensation at the multi-scale fusion junction. On the VisDrone2019 benchmark, FLD-RTDETR attains a mean Average Precision (mAP) of 50.2% (
) and 30.9% (
) at only 20.11M parameters and 86.4 giga floating-point operations (GFLOPs), improving over the RT-DETR-R18 baseline by 3.1 and 2.1 absolute points, respectively and reaching parity with RT-DETR-R50 at less than half its parameters and roughly two-thirds of its floating-point operations. Evaluation on the structurally different NWPU VHR-10 benchmark—on which the model is separately trained and tested—further indicates robustness across scale distributions and viewpoint geometries, providing a competitive accuracy/efficiency trade-off for high-fidelity small-object detection in aerial remote sensing.
Keywords
Aerial imagery; small-object detection; frequency–spatial attention; lightweight detection
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