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Updated: May 24, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Foggy water surface target detection model based on joint optimization.

Haihua Zhang1, Hongdong Wang2

  • 1State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.

Scientific Reports
|April 2, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a joint optimization dehazing target detection algorithm to improve image quality in hazy conditions. The enhanced algorithm significantly boosts the detection accuracy of small targets in challenging, low-visibility environments.

Keywords:
Dehazing networkFeature attentionJoint optimizationMixed attentionSmall target detectionWater surface target detection

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Haze significantly degrades water surface image quality, reducing contrast and blurring edges.
  • Accurate target detection in hazy scenes remains a challenge for computer vision systems.

Purpose of the Study:

  • To develop an improved dehazing target detection algorithm for enhanced performance in hazy conditions.
  • To specifically address the challenge of detecting small targets obscured by haze.

Main Methods:

  • Improved Feature Fusion Attention Network using Pyramid Squeeze Attention Block and Coordinate Attention for multi-scale feature extraction.
  • Incorporated skip connections with residual edges for enhanced feature recovery.
  • Utilized Depthwise Separable Convolution to optimize backbone inference speed.
  • Added a 1x1 convolutional block to improve small target detection capabilities.

Main Results:

  • Achieved 41.18% average accuracy on WSFOG_VisDrone, a 3% improvement over the original algorithm.
  • Reached 89.95% average accuracy on WSFOG_VOC, a 4.11% improvement.
  • Demonstrated effective improvement in detecting small targets within hazy scenes.

Conclusions:

  • The proposed joint optimization algorithm effectively enhances target detection accuracy in hazy environments.
  • The algorithm exhibits strong applicability and robustness for real-world scenarios with reduced visibility.