Scale-Aware Gated Routing Distillation for Lightweight Remote Sensing Object Counting
Year: 2026
Type: article
Abstract: Remote sensing object counting remains challenging for lightweight models due to large scale variation, dense object distribution, and complex background interference. Existing lightweight counting methods usually rely on static multi-scale fusion, which limits adaptive receptive-field selection under heterogeneous scenes. To address this issue, this paper proposes Scale-Aware Gated Routing Distillation (SAGRD), a lightweight framework that reformulates multi-scale feature fusion as an adaptive routing problem. At its core, a scale-aware gated routing pyramid generates dynamic branch weights from three complementary cues—global scale descriptors, branch-response feedback, and spatial routing signals—to adaptively reweight dilated receptive-field responses before weighted aggregation, enabling more flexible scale-aware representation under limited model capacity. In addition, a routing-oriented distillation strategy is introduced to transfer not only density estimation knowledge but also the teacher’s scale-selection distributions to the lightweight student, allowing the compact model to inherit structured receptive-field adaptation strategies. Extensive experiments on multiple remote sensing object counting datasets demonstrate that the proposed framework achieves a favorable accuracy-efficiency trade-off. On the RSOC Building benchmark, the student model achieves an MAE/RMSE of 4.712/6.367 using only 1.395 M parameters and 1.29 G FLOPs, closely approaching the teacher performance.
Source: Remote Sensing
DOI: 10.3390/rs18172939
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W7204950153