TL;DR
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.
HD-YOLO improves small-object detection with a compact parameter footprint, while direct hardware benchmarks remain necessary to establish deployment efficiency.
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.
The proposed framework features a redesigned cross-scale feature fusion module, CCFM-S2, which utilizes the SPD-Conv operator for information preserving downsampling and explicitly integrates high-resolution shallow features (S2 layer), thereby infusing indispensable spatial details into the feature hierarchy for small...
PDLL-YOLO comprises a P2–P3–P4 high-resolution prediction architecture and three core modules: the detail-structure-aware module (DSAM), the local-context enhanced fusion module (LCEF), and the local density hint module (LDH).
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.
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.
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