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Open Access

ARTICLE

HiFreq-DETR: A Hierarchical Framework Synergizing High-Resolution Injection and Frequency-Aware Multi-Scale Interaction for Tiny Object Detection

Linyu Dong1, Tao Li2, Hao Li2,*
1 School of Information Science & Engineering, Yunnan University, Kunming, China
2 Yunnan Communications Investment & Construction Group Co., Ltd., Kunming, China
* Corresponding Author: Hao Li. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.083042

Received 27 March 2026; Accepted 18 May 2026; Published online 08 June 2026

Abstract

While Transformer-based detectors excel in global modeling, their efficacy in unmanned aerial vehicle (UAV)-based tiny object detection is limited by information loss during aggressive downsampling and the lack of high-frequency structural cues. To bridge this gap, we propose HiFreq-DETR, a dedicated framework that optimizes the synergy between spatial fidelity and semantic discriminability. The core innovation lies in its hierarchical information preservation strategy, which employs a ResNeSt14d backbone coupled with an S2 spatial injection path to recover critical high-resolution structural anchors, and introduces a frequency-selective interaction module to decouple target saliency from background noise. Experimental results demonstrate the substantial value of our approach. On the VisDrone dataset, HiFreq-DETR significantly outperforms the baseline RT-DETR, achieving improvements of 3.9% in AP and 4.4% in APS, confirming its effectiveness for tiny object detection. Furthermore, an optimized lite variant is evaluated to challenge the limits of high-efficiency processing for resource-constrained scenarios, while superior gains on the HazyDet dataset validate the model’s structural robustness in adverse aerial environments. These findings establish HiFreq-DETR as a high-fidelity and versatile solution for complex remote sensing applications.

Keywords

UAV; tiny object detection; multi-scale feature interaction; frequency-selective attention; high-resolution representation learning; DETR

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