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Related Experiment Video

Updated: Jan 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

999

GMG-LDefmamba-YOLO: An Improved YOLOv11 Algorithm Based on Gear-Shaped Convolution and a Linear-Deformable Mamba

Yiming Yang1,2, Lingyu Yan1,2, Jing Wang1,2

  • 1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary

This study introduces GMG-LDefmamba-YOLO, an advanced object detection model for remote sensing. It enhances small target detection accuracy and speed in complex environments, outperforming existing methods.

Keywords:
UAV remote sensing imageYOLOv11gear shape convolutionlinear deformable mambasmall object detection

Related Experiment Videos

Last Updated: Jan 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

999

Area of Science:

  • Computer Vision
  • Remote Sensing Technology
  • Artificial Intelligence

Background:

  • Object detection in remote sensing and UAV imagery is vital but challenged by multi-scale, dense small targets and complex environmental noise.
  • Existing Mamba-based methods offer potential for long-range dependencies but require customization for remote sensing applications.

Purpose of the Study:

  • To develop an efficient and accurate object detection model tailored for remote sensing and UAV image analysis.
  • To address the limitations of current methods in detecting small, dense targets amidst complex backgrounds and noise.

Main Methods:

  • Proposed GMG-LDefmamba-YOLO with two core modules: Gaussian Mask Gear Convolution for enhanced small target feature extraction and background noise suppression.
  • Integrated a Linear Deformable Mamba module for adaptive target scale and spatial distribution modeling with reduced computational cost.

Main Results:

  • Achieved mAP50 scores of 70.91% on DOTA-v1.0, 77.94% on VEDAI, and 90.28% on USOD datasets.
  • Demonstrated superior performance compared to baseline and mainstream object detection methods.
  • Maintained lightweight characteristics for efficient deployment.

Conclusions:

  • GMG-LDefmamba-YOLO offers a significant advancement in object detection for remote sensing and UAV applications.
  • The model provides efficient technical support for critical tasks like remote sensing monitoring and UAV inspection.