TL;DR
Results verify that AARM effectively enhances the representation of small objects in complex remote sensing conditions of UAVs and achieves stable improvements in detection accuracy while maintaining high inference speed.
A context-gated dynamic perception framework that treats small-object feature degradation as a coupled problem of representation, fusion, and prediction and indicates a practical accuracy-efficiency trade-off for dense aerial small-object perception.
HSAR-DETR is proposed, a detection framework that jointly improves hierarchical feature representation, cross-scale refinement, and geometry-aware localization and experimental results on the VisDrone, RSOD, and TinyPerson datasets demonstrate improved detection performance.
HD-YOLO improves small-object detection with a compact parameter footprint, while direct hardware benchmarks remain necessary to establish deployment efficiency.
A gated feature fusion net is designed to reduce model parameters and a small object detection pyramid is implemented to preserve high-resolution details and integrate a mini-kernel that utilizes a lossless down-sampling module for deep feature optimization.
This paper proposes MPC-DETR, a Multi-scale Patch Context Transformer that is based on RT-DETR and suggests that MPC-DETR provides precise, strong, and efficient small-object detection in complicated UAV images.
MD-YOLO is proposed, an improved object detection model tailored for UAV scenarios, built upon the YOLO26 baseline, that incorporates three lightweight modules—IMO, DS-SPPF, and HPConv to optimize backbone feature extraction, multi-scale contextual aggregation, and Neck downsampling, thereby enhancing the model’s detec...
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