UHR-DETR delivers 2.8% higher mAP and 10x faster inference than sliding-window baselines for small object detection in UHR remote sensing imagery on a single 24GB GPU.
Detection and tracking meet drones challenge
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TinySet-9M dataset and DEAL point-prompted framework deliver 31.4% relative AP75 gain over supervised baselines for small object detection with one click at inference and generalization to unseen categories.
DFIR-DETR augments RT-DETR with frequency-domain iterative refinement and dynamic feature aggregation, reporting 92.9% mAP50 on NEU-DET and 51.6% on VisDrone at 11.7M parameters and 47.2 GFLOPs.
citing papers explorer
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UHR-DETR: Efficient End-to-End Small Object Detection for Ultra-High-Resolution Remote Sensing Imagery
UHR-DETR delivers 2.8% higher mAP and 10x faster inference than sliding-window baselines for small object detection in UHR remote sensing imagery on a single 24GB GPU.
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Generalized Small Object Detection:A Point-Prompted Paradigm and Benchmark
TinySet-9M dataset and DEAL point-prompted framework deliver 31.4% relative AP75 gain over supervised baselines for small object detection with one click at inference and generalization to unseen categories.
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DFIR-DETR: Frequency-Domain Iterative Refinement and Dynamic Feature Aggregation for Small Object Detection
DFIR-DETR augments RT-DETR with frequency-domain iterative refinement and dynamic feature aggregation, reporting 92.9% mAP50 on NEU-DET and 51.6% on VisDrone at 11.7M parameters and 47.2 GFLOPs.