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Related Experiment Videos

MicroSight-DETR: spatial-preserving real-time transformer with multi-domain fusion for UAV micro-object detection.

Junhao Guo1, Jinhao Jiang1, Zijing Yang2,3

  • 1School of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, 102600, Beijing, China.

Scientific Reports
|July 9, 2026
PubMed
Summary

This study introduces MicroSight-DETR, an improved real-time detection model for small targets in UAV imagery, significantly boosting accuracy by addressing key degradation issues in existing models.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Remote Sensing

Background:

  • UAV monitoring faces challenges with low detection accuracy for small targets.
  • Existing models like RT-DETR exhibit stage-specific degradation in aerial imagery analysis.

Purpose of the Study:

  • To develop an enhanced real-time detection model, MicroSight-DETR, for improved small target detection in UAV scenarios.
  • To address identified degradation mechanisms within the RT-DETR pipeline.

Main Methods:

  • Introduced Global Efficient Modeling (GEM) backbone for global feature modeling with linear complexity.
  • Developed Multi-domain Adaptive Fusion Dynamics (MAFD) encoder for adaptive spatial-frequency dual-domain fusion.
  • Implemented Spatial Preserving Aggregation with Multi-scale (SPAM) neck to preserve shallow detail features.

Main Results:

  • MicroSight-DETR achieved 51.3% mAP@0.5 (9.9% relative improvement) and 31.8% mAP@0.5:0.95 (12% increase) on the VisDrone2019 dataset.
  • Demonstrated additive improvements from the three proposed modules through ablation studies.
  • Achieved real-time inference at 78 FPS with 16.1M parameters and 64.5 GFLOPs.

Conclusions:

  • MicroSight-DETR offers a robust solution for small target detection in UAV imagery.
  • The complementary design of GEM, MAFD, and SPAM modules effectively mitigates degradation issues.
  • The model provides efficient and accurate real-time processing for aerial surveillance applications.