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A Complex Environmental Water-Level Detection Method Based on Improved YOLOv5m.

Jiadong Li1, Chunya Tong1, Hongxing Yuan1

  • 1School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo 315211, China.

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|August 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an improved YOLOv5m model for accurate water-level recognition, achieving less than 2 cm error in complex real-world conditions. The method enhances reliability for practical water-level identification applications.

Keywords:
complex environmentsimproved YOLOv5mskew correctionwater-level recognitionwater-level ruler

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Area of Science:

  • Computer Vision
  • Environmental Monitoring
  • Image Processing

Background:

  • Traditional water-level recognition methods lack accuracy in challenging environments.
  • This limitation hinders their practical application and reliability in real-world scenarios.

Purpose of the Study:

  • To develop a robust and accurate water-level identification system.
  • To overcome the limitations of existing methods in complex environmental conditions.

Main Methods:

  • An improved YOLOv5m model combined with contextual knowledge for water-level identification.
  • Adaptive threshold Canny operator and Hough transform for image skew correction.
  • Linear regression for water-level value prediction based on segmented gauge data.

Main Results:

  • The proposed method achieves an average error of less than 2 cm.
  • Demonstrated high accuracy across diverse real-world conditions including varying light, occlusion, day, and night.
  • Successfully extracts and refines water-level gauge information.

Conclusions:

  • The novel approach significantly improves water-level recognition accuracy and reliability.
  • The system is practical for real-world applications due to its robustness.
  • This method offers a reliable solution for environmental water-level monitoring.