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Related Experiment Video

Updated: May 24, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Efficient real time small object detection framework in aerial images using edge awareness and dynamic convolution.

Tieshan Zhang1,2, Shaoyuan Xi3, Dongyue Chen4

  • 1College of Information Science and Engineering, Northeastern University ShenYang, LiaoNing, 110819, China. zts336699@126.com.

Scientific Reports
|May 22, 2026
PubMed
Summary
This summary is machine-generated.

GDD-YOLO enhances real-time small object detection by using a Global Edge Information Transfer module and a Dynamic Inception Mixer. This efficient framework improves accuracy while reducing computational costs for edge devices.

Keywords:
Computational efficiencyDynamic convolutionEdge awarenessReal-time detectionSmall object detection

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Last Updated: May 24, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

  • Computer Vision
  • Deep Learning
  • Object Detection

Background:

  • Small object detection in high-resolution images is difficult due to weak appearance cues and loss of detail in conventional methods.
  • Existing detectors often increase computational cost and latency, hindering real-time application on resource-constrained devices.

Purpose of the Study:

  • To propose GDD-YOLO, an efficient real-time framework for small object detection.
  • To improve accuracy and efficiency for detecting small objects on edge devices.

Main Methods:

  • Introduced a Global Edge Information Transfer (GEIT) module to propagate multiscale edge cues for better localization.
  • Developed a Dynamic Inception Mixer (DIM) for input-adaptive feature aggregation with reduced complexity.
  • Designed a lightweight detection head (DECDH) to preserve information while minimizing parameters.

Main Results:

  • GDD-YOLO achieved 26.2% AP on the VisDrone dataset, surpassing YOLOv11-S by 2.4%.
  • Reduced parameter count by 16.8% and computational cost by 14.6% compared to YOLOv11-S.
  • Demonstrated superior performance in small object detection accuracy and inference efficiency.

Conclusions:

  • GDD-YOLO offers an effective balance between detection accuracy and inference efficiency for real-time small object detection.
  • The proposed framework is suitable for deployment on edge devices with limited resources.
  • The GEIT module, DIM, and DECDH contribute to enhanced performance in challenging small object detection scenarios.