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An AI based smart-phone system for asbestos identification.

Michael Rolfe1, Samantha Hayes2, Meaghan Smith1

  • 1Department of Chemistry and Biotechnology and Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology Melbourne, VIC 3122, Australia.

Journal of Hazardous Materials
|November 2, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a smartphone-based image recognition system for asbestos identification. The system achieved 90% accuracy, offering a portable and cost-effective solution for detecting asbestos materials.

Keywords:
AsbestosConstitutional Neutral NetworkHazardous Materials IdentificationImage Recognition

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Area of Science:

  • Environmental Science
  • Materials Science
  • Computer Science

Background:

  • Asbestos identification is crucial for environmental and economic reasons.
  • Current methods rely on laboratory analysis with light microscopy and specialized mounting.
  • A need exists for more accessible and portable asbestos detection methods.

Purpose of the Study:

  • To develop a smartphone-based image recognition system for asbestos identification.
  • To evaluate the effectiveness of portable microscopy combined with deep learning for this task.
  • To compare the performance of different convolutional neural network (CNN) models.

Main Methods:

  • Utilized a portable 30x microscope with a smartphone camera.
  • Trained a deep learning model using 7328 images from over 1000 asbestos cement sheet samples.
  • Tested and compared three CNN models: ResNet101, InceptionV3, and VGG_16.

Main Results:

  • ResNet101 achieved the highest accuracy (98.46%) with a loss of 3.8%.
  • The phone-based system correctly identified asbestos distinctiveness 90% of the time without specialized mounting.
  • ResNet101 demonstrated superior performance compared to other tested deep learning models.

Conclusions:

  • A portable smartphone-based system can effectively identify asbestos.
  • Deep learning, particularly ResNet101, offers a promising approach for accurate asbestos detection.
  • This technology provides a more accessible and potentially cost-effective alternative to traditional laboratory methods.