Balancing small object detection performance with model lightweighting remains a critical challenge in the remote sensing domain. To address the massive computational and parameter overhead of existing algorithms, we propose the hybrid scale dynamic detection transformer (HSD-DETR). This lightweight detector incorporates four core innovations to effectively enhance feature extraction for small objects. First, to reduce costs without compromising performance, we design a hybrid convolution and selective scanning fusion (HCSS-Fusion) module to reconstruct the backbone, combining local convolution with global linear scanning. Second, to preserve fine-grained information, we introduce a space-to-depth mixer (SPDMixer) to achieve pixel-level lossless downsampling. Third, to mitigate background interference and enhance small object representation, we develop a dynamic sparse adaptive intra-scale feature interaction (DSAIFI) module, employing a gating mechanism to dynamically select informative spatial tokens. Finally, to improve the localization precision for small objects, we propose the rational-focal minimum point distance intersection over union (RF-MPDIoU) loss, utilizing a non-linear mapping to dynamically modulate sample weights. Experimental results on public benchmarks confirmed that, compared to mainstream models, HSD-DETR achieves highly competitive accuracy while significantly reducing parameter scale and theoretical computational complexity. Ultimately, this research provides a lightweight and robust algorithmic solution for the field of remote sensing object detection.
Xu et al. (Thu,) studied this question.