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Related Experiment Video

Updated: Feb 27, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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YOLO11s-UAV: An Advanced Algorithm for Small Object Detection in UAV Aerial Imagery.

Qi Mi1,2,3,4, Jianshu Chao2,3,4, Anqi Chen1,2,3,4

  • 1College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China.

Journal of Imaging
|February 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces YOLO11s-UAV, an improved algorithm for detecting small objects in aerial imagery from unmanned aerial vehicles (UAVs). The enhanced model significantly boosts detection accuracy while reducing computational load for real-time applications.

Keywords:
UAV aerial imageryVisDrone-DET2019YOLO11sfeature pyramidlightweight object detectorsmall object detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Unmanned aerial vehicles (UAVs) are increasingly vital for applications like agriculture and search and rescue.
  • Detecting small objects in aerial imagery presents challenges due to sparse pixels, complex backgrounds, and limited onboard computational power.
  • Existing object detection algorithms struggle with the unique demands of aerial surveillance.

Purpose of the Study:

  • To develop an improved UAV-based small object detection algorithm for enhanced performance in aerial imagery.
  • To address challenges of small object detection, complex backgrounds, and computational constraints in UAV applications.
  • To create a more efficient and accurate model for real-time object detection on UAV platforms.

Main Methods:

  • Introduced Content-Aware Reassembly and Interaction Feature Pyramid Network (CARIFPN) for superior small object feature detection and reduced network complexity.
  • Implemented Space-to-Depth for Dilation-wise Residual Convolution (S2DResConv) in the backbone to prevent information loss and capture multi-scale context.
  • Integrated Flexible SimAM (FlexSimAM), a parameter-free attention module, to efficiently enhance small object features in complex aerial scenes.

Main Results:

  • Achieved a 7.8% improvement in mAP@0.5 on the VisDrone-DET2019 validation set (46.0%) and a 5.9% increase on the test set (37.3%) compared to the YOLO11s baseline.
  • Reduced model parameters by 55.3% and demonstrated significant improvements on TinyPerson (7.2%) and UAVDT-DET (3.0%) datasets.
  • Attained 33 FPS on the NVIDIA Jetson Orin NX SUPER, confirming real-time onboard processing feasibility with a 21.4% reduction in processing time.

Conclusions:

  • The proposed YOLO11s-UAV algorithm effectively enhances small object detection in aerial imagery.
  • The novel CARIFPN, S2DResConv, and FlexSimAM modules contribute to improved accuracy and efficiency.
  • The model's performance and reduced complexity make it suitable for real-time onboard UAV applications.