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An Adaptive Parameter Optimization Deep Learning Model for Energetic Liquid Vision Recognition Based on Feedback

Lu Chen1, Yuhao Yang1, Tianci Wu1

  • 1School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
Summary

A new deep learning model, DBN-AGS-FLSS, precisely detects liquid flow and viscosity in real-time. This computer vision approach enhances industrial monitoring accuracy for diverse and reflective liquids.

Keywords:
Adaptive Genetic Selectordeep genetic feedbackenergetic liquidintegrated deep learning modelviscosity visual recognition

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Industrial Monitoring

Background:

  • Precise liquid flow and viscosity detection is vital for industrial and environmental monitoring.
  • Traditional methods face challenges with diverse liquid samples and reflective properties of energetic liquids.

Purpose of the Study:

  • To develop a novel, high-precision, real-time liquid surface pointer detection model.
  • To address complexities from sample diversity and reflective properties using computer vision and deep learning.

Main Methods:

  • Proposed the DBN-AGS-FLSS integrated deep learning model.
  • Combined Deep Belief Networks (DBN), Feedback Least-Squares SVM (FLSS), and Adaptive Genetic Selectors (AGS).
  • Utilized bilateral filtering, adaptive contrast enhancement, and a feedback mechanism for parameter optimization.

Main Results:

  • Achieved high performance metrics: 99.37% accuracy, 99.36% precision, 99.16% F1 score, and 99.36% recall.
  • Demonstrated a rapid inference speed of 1.5 ms/frame.
  • Validated superior performance in complex detection scenarios.

Conclusions:

  • The DBN-AGS-FLSS model offers practical and reliable liquid detection.
  • Opens new avenues for real-time industrial monitoring and automated systems.
  • Provides a valuable reference for future computer vision detection technologies.