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A Scale-Adaptive Aggregation and Multi-Domain Feature Fusion Architecture for Small-Target Detection in UAV Aerial

Zhiwei Sun1, Guanglei Zhang1, Yuxin Xing1

  • 1College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300457, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
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This summary is machine-generated.

This study introduces MSCM-YOLO, a lightweight framework for detecting small objects in drone imagery. It significantly improves accuracy by enhancing feature extraction and fusion, offering a practical solution for aerial monitoring.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Detecting small objects in Unmanned Aerial Vehicle (UAV) imagery is challenging due to scale variations, dense distribution, and complex backgrounds.
  • Existing methods struggle with small objects that occupy few pixels and are obscured by clutter.

Purpose of the Study:

  • To develop an effective and lightweight detection framework for small objects in UAV aerial monitoring.
  • To address limitations in feature extraction, multi-scale fusion, and target-background discrimination for small object detection.

Main Methods:

  • Proposes MSCM-YOLO, a UAV-oriented lightweight detection framework based on YOLOv11.
  • Integrates a P2 detection head for high-resolution features, a lightweight backbone with Mobile Bottleneck Convolution (MBConv), Scale-Adaptive Attention Fusion (SAF) with Channel-Adaptive Projection (CAP), and Multi-Domain Feature Attention Fusion (MDFAF).
Keywords:
UAV imageryaerial monitoringmulti-domain fusionscale-adaptive fusionsmall object detection

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Main Results:

  • MSCM-YOLO achieved mAP50 of 44.41% and mAP50:95 of 27.13% on the VisDrone2019 dataset, outperforming the YOLOv11 baseline by 10.77% and 7.22%, respectively.
  • Demonstrated consistent improvements on UAVDT, DIOR, and AI-TOD datasets, confirming robustness and generalization.
  • Achieved significant performance gains while maintaining a balanced computational profile suitable for UAV deployment.

Conclusions:

  • MSCM-YOLO offers an effective and practical solution for accurate small object detection in complex UAV scenes.
  • The proposed innovations enhance feature preservation, extraction, multi-scale integration, and target-background discrimination.
  • The framework shows strong potential for real-world aerial monitoring applications.