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March 13, 2026Remote SensingOpen Access

Addressing Dense Small-Object Detection in Remote Sensing: An Open-Vocabulary Object Detection Framework

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Authors

MJMenghan JuTarget (United States)YFYingchao FengTarget (United States)WDW. DiaoTarget (United States)

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Implication

Open-vocabulary object detection improves localization accuracy in remote sensing imagery, suggesting enhanced detection methods are needed.

Key Points

  • The research aims to enhance dense small-object detection in remote sensing imagery using an open-vocabulary approach.
  • Developed RS-DINO framework for object detection.
  • Incorporated multi-scale large-kernel attention for better feature extraction.
  • Implemented cross-modal feature fusion with bidirectional cross-attention.
  • Applied language-guided query selection to improve detection accuracy.
  • Used a convolutional gated feedforward network for feature fusion.
  • RS-DINO demonstrated a 3.5% accuracy improvement on DIOR dataset.
  • Achieved a 3.7% increase in accuracy on DOTA v2.0 dataset.
  • Surpassed existing methods by 4.0% in accuracy on NWPU-VHR10 dataset.

Cite This Study

Ju et al. (2026) studied this question.

synapsesocial.com/papers/69b3ac1d02a1e69014ccd84ahttps://doi.org/10.3390/rs18060851
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