MSRS-DETR: End-to-End Object Detection for Multi-Scale Remote Sensing

Remote sensing imagery (RSI) object detection is critical to many applications, yet mainstream detectors analyse only spatial features and, because of spectral bias, fail to learn high-frequency information adequately, resulting in performance bottlenecks under cluttered backgrounds, distractors, and multi-scale targets, especially small ones. To break these limitations, we propose MSRS-DETR, an end-to-end framework that deeply fuses spatial and frequency cues. The approach introduces three key innovations: (1) C2fFATNET, a frequency-attention-enhanced lightweight residual backbone that provides richer dual-domain features with fewer parameters; (2) an Entanglement Transformer Block (ETB) in the encoder that refines deep semantics via cross-domain frequency–spatial interaction and suppresses background interference; and (3) S2-CCFF, a shallow-feature-extended bidirectional fusion path that markedly improves the retention and utilisation of fine details for small objects. Experiments on HRSC2016 and ShipRSImageNet demonstrate the effectiveness and generalisation of this spatial–frequency paradigm: relative to the baseline, MSRS-DETR reduces parameters by 29.1%, boosts inference speed by 12.4% and 8.4%, and raises mAP50-95 by 1.69% and 2.16%, respectively.