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Related Experiment Videos

MDCL-DETR: Multi-Domain Enhancement and Cross-Layer Feature Fusion for Small Object Detection.

Tianran Hao1, Xiao Zhang1, Bing Zhou1

  • 1School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel detection Transformer for small object detection in uncrewed aerial vehicle (UAV) imagery. The proposed method enhances features and fuses them across layers, significantly improving detection accuracy for small objects.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Remote Sensing

Background:

  • Small object detection in UAV imagery faces challenges due to limited pixels, weak feature representation, and background interference.
  • Existing methods struggle with preserving details and contextual information for small objects.

Purpose of the Study:

  • To develop an advanced detection Transformer for improved small object detection in UAV imagery.
  • To enhance feature representation and contextual modeling for better performance.

Main Methods:

  • Proposed a multi-domain enhancement and cross-layer feature fusion detection Transformer (MDCL-DETR).
  • Introduced a multi-domain enhancement module (MDEM) for fusing spatial and frequency-domain features.
  • Implemented a cross-layer feature extraction module (CLEM) for multi-scale feature aggregation.
Keywords:
feature fusionmulti-domain enhancementsmall object detectionuncrewed aerial vehicle (UAV)

Related Experiment Videos

  • Utilized a gated Mamba fusion module (GMFM) for long-range dependency modeling and dynamic feature fusion.
  • Incorporated a fine-grained enhancement module (FGEM) for reinforcing object details.

    • Main Results:

    • The MDCL-DETR method achieved mAP50 scores of 54.1% on VisDrone2019 and 56.2% on AI-TOD.
    • Demonstrated significant improvements in detecting small objects with weak features and background interference.
    • Validated the effectiveness and generalization capabilities of the proposed approach.

    Conclusions:

    • The proposed MDCL-DETR effectively addresses the challenges of small object detection in UAV imagery.
    • The integration of multi-domain enhancement, cross-layer fusion, and Mamba architecture significantly boosts detection performance.
    • The method shows strong potential for real-world applications in aerial surveillance and monitoring.