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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
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EAGLE-DET: Edge-Aware Global-Local Enhancement for Small Object Detection in UAV Aerial Imagery.

Yimeng Tao1, Yan Ding1, Bo Mo1

  • 1School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

Detecting small objects in UAV imagery is challenging. EAGLE-DET, a new framework, uses sparse multi-scale attention to enhance feature extraction, fusion, and reconstruction, improving accuracy and efficiency.

Keywords:
UAV object detectionedge enhancementmulti-scale feature fusionsmall object detectionsparse attention mechanism

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Remote Sensing

Background:

  • Small object detection in Unmanned Aerial Vehicle (UAV) aerial imagery faces challenges like sparse pixel representation and ambiguous boundaries.
  • Existing deep detection networks suffer from edge attenuation, semantic conflict, and detail loss during forward propagation.
  • Current methods lack collaborative and stage-aware strategies to repair these degradation stages.

Purpose of the Study:

  • To propose EAGLE-DET, a novel detection framework addressing critical degradation stages in small object detection for UAV imagery.
  • To enhance small object detection by preserving edge representations, resolving semantic conflicts, and recovering spatial detail fidelity.
  • To achieve an optimal accuracy-efficiency trade-off in UAV-based small object detection.

Main Methods:

  • EAGLE-DET framework utilizes sparse multi-scale attention and refined transformation.
  • Key modules include Cross-stage Multi-resolution Edge Enhancement Network (CMENet) for edge preservation, Attention-guided Multi-scale Feature Fusion Network (AMFFN) for semantic conflict resolution, and Enhanced Upsampling with Channel Bridging and Spatial Coordination (EUCBSC) for detail recovery.
  • Methods involve adaptive high-low frequency decomposition, pyramidal sparse attention, multi-scale spatial decoupling, and bidirectional channel shift mixing.

Main Results:

  • EAGLE-DET demonstrated significant improvements on VisDrone-2019, achieving 4.5% AP50 and 2.9% AP50:95 increases over the baseline.
  • The framework maintains a high inference speed of 71.7 FPS.
  • Experiments on VisDrone-2019, UAVDT, and DOTA1.0 datasets validated the framework's effectiveness.

Conclusions:

  • EAGLE-DET effectively addresses degradation stages in small object detection for UAV imagery.
  • The proposed framework achieves a superior balance between detection accuracy and computational efficiency.
  • EAGLE-DET offers a promising solution for real-world applications requiring high-performance small object detection from aerial platforms.