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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Clustering-Based Thermography for Detecting Multiple Substances Under Large-Scale Floating Covers.

Yue Ma¹, Benjamin Steven Vien¹, Thomas Kuen²

¹Department of Mechanical & Aerospace Engineering, Monash University, Clayton, VIC 3800, Australia.

Sensors (Basel, Switzerland)

|January 8, 2025

Summary

This study introduces a new method using infrared thermal imaging and clustering to monitor waste digestion under high-density polyethylene (HDPE) covers in sewage plants. The technique effectively identifies different materials beneath covers, improving waste management decisions.

Keywords:

floating covers high-density polyethylene geomembranes image segmentation structural health monitoring thermal imaging monitoring water treatment plant

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Area of Science:

Environmental Engineering
Waste Management Technologies
Geospatial Analysis

Background:

Effective monitoring of waste substrate digestion under geomembranes is crucial for optimizing sewage treatment processes.
Traditional methods for assessing conditions beneath opaque covers are often limited and labor-intensive.
Understanding material distribution is key to managing scum and biogas collection.

Purpose of the Study:

To develop and validate a novel, non-invasive method for monitoring waste substrate digestion under high-density polyethylene (HDPE) geomembranes.
To integrate infrared thermal imaging with clustering algorithms for predictive analysis of subsurface conditions.
To enhance decision-making in sewage treatment management through improved insights into scum and biogas processes.

Main Methods:

Utilizing solar radiation as a natural excitation source to assess thermal responses of HDPE covers.
Applying cooling constants to reconstruct thermal images and analyze transient temperature variations.
Employing clustering algorithms to segment and identify different material states beneath the covers.
Conducting laboratory experiments to validate the algorithm's classification accuracy.

Main Results:

Demonstrated effectiveness of the integrated thermal imaging and clustering approach in accurately classifying varied regions beneath geomembranes.
Successful identification of different material states by analyzing temperature variations induced by natural thermal excitations.
Validation of the algorithm's capability in laboratory settings.

Conclusions:

The developed thermography-based method offers a promising, non-invasive solution for monitoring waste substrate digestion.
This approach provides critical insights into scum characteristics and biogas collection, aiding in efficient sewage treatment.
Future work includes field validation and development of an on-site, continuous monitoring system.