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Related Experiment Video

Updated: Apr 2, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

MFR-YOLO: advancing UAV object detection with multi-scale feature refinement via deformable convolution and global

Jiaxiang Ge¹, Huangming Lv², Yaoxuan Guo¹

¹School of Computer Science Technology, Zhejiang Normal University, Jinhua, China.

Scientific Reports

|March 31, 2026

Summary

This study introduces MFR-YOLO, an enhanced object detection model for Unmanned Aerial Vehicle (UAV) imagery. MFR-YOLO significantly improves small object detection and overall accuracy in complex scenes.

Keywords:

Deformable convolution Global attention Multi-scale feature extraction Object detection YOLO

Related Experiment Videos

Last Updated: Apr 2, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Unmanned aerial vehicle (UAV) imagery presents significant challenges for object detection due to extreme scale variance, high small-object density, and geometric distortions.
Existing state-of-the-art detectors, including the YOLO series, struggle to effectively address these complexities.

Purpose of the Study:

To develop an enhanced object detection model, MFR-YOLO, specifically designed to overcome the limitations of current detectors in complex UAV scenes.
To improve the detection accuracy, particularly for small objects, while maintaining real-time efficiency.

Main Methods:

A multi-scale feature refinement network is proposed, incorporating SPD-Conv for detail preservation and DCNv4 for adaptive receptive fields.
Standard convolutions are replaced with DCNv4 layers to enhance feature propagation.
A lightweight Global Attention Module and a Pyramid-Pooling Attention mechanism are integrated to refine features and fuse multi-scale contextual information.

Main Results:

MFR-YOLO demonstrates superior overall detection accuracy on the VisDrone2021 and UA-DETRAC benchmarks.
Significant improvements in small object detection capabilities were observed.
The model achieves a favorable balance between high accuracy and real-time processing efficiency.

Conclusions:

The proposed MFR-YOLO model effectively addresses the challenges of object detection in complex UAV imagery.
The integration of multi-scale feature refinement, attention mechanisms, and adaptive convolutions provides a robust technical framework for advanced object detection.