FSDETR: Frequency-Spatial Feature Enhancement for Small Object Detection

• URL: http://arxiv.org/abs/2604.14884v1
• Date: Thu, 16 Apr 2026 11:22:13 GMT
• Title: FSDETR: Frequency-Spatial Feature Enhancement for Small Object Detection
• Abstract summary: Small object detection remains a significant challenge due to feature degradation from downsampling.<n>This paper proposes FSDETR, a frequency-spatial feature enhancement framework built upon the RT-DETR baseline.<n>With only 14.7M parameters, FSDETR achieves 13.9% APS on VisDrone 2019 and 48.95% AP50 tiny on TinyPerson.
• Score: 1.5054442506653827
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Small object detection remains a significant challenge due to feature degradation from downsampling, mutual occlusion in dense clusters, and complex background interference. To address these issues, this paper proposes FSDETR, a frequency-spatial feature enhancement framework built upon the RT-DETR baseline. By establishing a collaborative modeling mechanism, the method effectively leverages complementary structural information. Specifically, a Spatial Hierarchical Attention Block (SHAB) captures both local details and global dependencies to strengthen semantic representation. Furthermore, to mitigate occlusion in dense scenes, the Deformable Attention-based Intra-scale Feature Interaction (DA-AIFI) focuses on informative regions via dynamic sampling. Finally, the Frequency-Spatial Feature Pyramid Network (FSFPN) integrates frequency filtering with spatial edge extraction via the Cross-domain Frequency-Spatial Block (CFSB) to preserve fine-grained details. Experimental results show that with only 14.7M parameters, FSDETR achieves 13.9% APS on VisDrone 2019 and 48.95% AP50 tiny on TinyPerson, showing strong performance on small-object benchmarks. The code and models are available at https://github.com/YT3DVision/FSDETR.

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