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Related Experiment Video

Updated: Jun 6, 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

A lightweight remote sensing small-object detection approach with scale-based dynamic loss and efficient multi-scale

Anying Xu1, Xuanyu Wu2,3, Wuzhong Yang1

  • 1School of Mechanical Engineering, Guizhou University, Guiyang, 550025, China.

Scientific Reports
|June 4, 2026
PubMed
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This study introduces a lightweight deep learning model for accurate small-object detection in remote sensing. The approach enhances efficiency and precision for real-time applications on edge devices.

Area of Science:

  • Computer Vision
  • Remote Sensing
  • Machine Learning

Background:

  • Small-object detection in remote sensing is vital but challenging due to limited features, complex backgrounds, and localization errors.
  • Existing methods struggle with scale variation and occlusion, impacting accuracy in real-world scenarios.

Purpose of the Study:

  • To develop a lightweight and efficient approach for accurate small-object detection in remote sensing imagery.
  • To improve target-background discrimination and localization accuracy for enhanced automated processes.

Main Methods:

  • Proposed a lightweight backbone (A2C2fLite) and an Efficient Multi-scale Attention (EMA) module.
  • Integrated a Scale-based Dynamic (SD) loss function to handle scale variation and occlusion.
  • Evaluated the approach on a remote sensing dataset, comparing it against baseline YOLOv13.

Related Experiment Videos

Last Updated: Jun 6, 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

Main Results:

  • Achieved 93.6% precision, 86.6% recall, and 92.6% mAP with only 2.0M parameters and 5.7 GFLOPs.
  • Demonstrated a superior accuracy-efficiency trade-off, reducing parameters by 20% and GFLOPs by 8.1% compared to the baseline.
  • Visualization confirmed suppressed missed alarms and refined bounding-box localization.

Conclusions:

  • The proposed lightweight design offers practical advantages for real-time small-object detection on resource-constrained edge devices.
  • The approach provides an efficient, robust, and highly deployable solution for complex remote sensing applications.
  • This work advances automated processes in environmental monitoring and aerial surveillance.