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I-YOLOv11n: A Lightweight and Efficient Small Target Detection Framework for UAV Aerial Images
Yukai Ma1, Caiping Xi1, Ting Ma1
1College of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
Sensors (Basel, Switzerland)
|August 14, 2025
Summary
This study introduces I-YOLOv11n, a lightweight algorithm for Unmanned Aerial Vehicle (UAV) small target detection. It enhances accuracy and reduces model size for better real-time performance in critical applications.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Unmanned Aerial Vehicle (UAV) applications require accurate and real-time small target detection.
- Existing algorithms struggle with small target representation, computational overhead, and deployment adaptability.
Purpose of the Study:
- To propose a lightweight algorithm, I-YOLOv11n, for improved UAV small target detection.
- To enhance feature representation and compress model structure for efficiency.
Main Methods:
- Designed RFCBAMConv module combining deformable convolution and channel-spatial attention.
- Developed STCMSP context multiscale pyramid and Transformer-DyHead hybrid detection head.
- Implemented collaborative lightweight strategy including knowledge distillation, channel pruning, and anchor optimization.
Main Results:
- Achieved a model size of 3.87 M parameters and 14.7 GFLOPs.
- Improved detection accuracy, with mAP@0.5 and mAP@0.5:0.95 increasing by 7.1% and 4.9% respectively compared to YOLOv11n.
- Maintained real-time processing capabilities.
Conclusions:
- I-YOLOv11n offers significant advantages in accuracy, lightweight design, and deployment adaptability for UAV small target detection.
- The proposed methods effectively address the limitations of existing algorithms.
- Validated performance on VisDrone, AI-TOD, and SODA-A datasets.
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