FSDC-DETR: A Frequency-Spatial Domain Collaborative DETR for Small-Object Detection

Small object detection (SOD) remains a challenging task inreal-world applications. Despite recent advances, existing detectors re-main limited by rigid processing that entangle spatial aggregation withimplicit frequency aliasing and truncation, leading to inadequate preser-vation of high-frequency components for SOD. To tackle these limita-tions, we propose a Frequency-Spatial Domain Collaborative DetectionTransformer (FSDC-DETR), a novel collaborative framework that ex-plicitly models complementary spatial and frequency representations.Specifically, we first introduce Dual-Branch Frequency-Spatial AdaptiveFusion (DBFSAF) to enhance frequency diversity and adaptively cap-ture frequency-spatial domain discriminative representations. Buildingon these representations, a frequency-spatial interaction scheme is fur-ther explored within the hybrid encoder to enable progressive featurepropagation to the decoder. In particular, structure-aware frequency-spatial aggregation is achieved through Shunt Frequency-Spatial Fea-ture Fusion (SFS-FF), establishing bidirectional interaction and pro-gressive cross-scale propagation between frequency and spatial repre-sentations for coherent discriminative modeling. Meanwhile, informativehigh-frequency responses are preserved during scale transitions throughFrequency-Spatial Dynamic Downsampling (FSD-Down), thereby mini-mizing frequency degradation throughout multi-scale fusion for the pre-cise SOD. Experimental results demonstrate that FSDC-DETR achievesstate-of-the-art performance, improving AP by 6.4 on VisDrone-DET2019and 6.6 on AITODv2, with gains of 6.8 and 6.9 AP for small objects. Thecode is available at https://github.com/nevereverinsomnia/FSDC-DETR.