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Updated: Jun 7, 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 Net with Dual-Path Feature Enhancer and Bidirectional Gated Fusion for Cloud Detection.

Yan Mo1,2, Puhui Chen3, Shaowei Bai2

  • 1College of Aeronautics Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

This study presents a lightweight network for efficient cloud detection in remote sensing images. The model achieves high accuracy while significantly reducing computational cost, making it suitable for resource-limited applications.

Keywords:
bidirectional gated fusioncloud detectiondual-path feature enhancerlightweight network

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Last Updated: Jun 7, 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:

  • Remote Sensing
  • Computer Vision
  • Artificial Intelligence

Background:

  • Cloud detection is crucial for remote sensing image analysis.
  • Existing deep learning models are often computationally intensive, limiting their use in edge computing.

Purpose of the Study:

  • To develop a lightweight network for accurate and efficient cloud detection.
  • To address the trade-off between accuracy and computational cost in remote sensing cloud detection.

Main Methods:

  • Introduced a dual-path feature enhancer for multi-scale feature extraction and fusion.
  • Developed a bidirectional gated fusion module with attention and dynamic convolution for adaptive feature integration.

Main Results:

  • Achieved 96.31% overall accuracy and 92.82% mean intersection-over-union on the HRC_WHU dataset.
  • Demonstrated a low computational cost of 12.04 GFLOPs, outperforming state-of-the-art methods.

Conclusions:

  • The proposed lightweight network effectively balances high detection performance with computational efficiency.
  • Offers a practical solution for real-time, lightweight cloud detection in high-resolution remote sensing imagery.