Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

An acoustic signal-to-image conversion integrated convolutional neural network model for egg crack detection.

British poultry science·2025
Same author

Enhanced dataset synthesis using conditional generative adversarial networks.

Biomedical engineering letters·2023
Same author

Emotion recognition using time-frequency ridges of EEG signals based on multivariate synchrosqueezing transform.

Biomedizinische Technik. Biomedical engineering·2021
Same author

Breast Cancer Detection with Reduced Feature Set.

Computational and mathematical methods in medicine·2015

Related Experiment Video

Updated: Sep 12, 2025

Ovarian Cancer Detection Using Photoacoustic Flow Cytometry
09:18

Ovarian Cancer Detection Using Photoacoustic Flow Cytometry

Published on: January 17, 2020

6.1K

Photoacoustic signal to image based convolutional neural network for defect detection.

Zekeriya Balcı1, Ahmet Mert2

  • 1Çaldıran Vocational High School, Van Yüzüncü Yıl University, Van 65080, Türkiye.

The Review of Scientific Instruments
|August 8, 2025
PubMed
Summary

This study introduces a new method using photoacoustic (PA) signals and convolutional neural networks (CNNs) for material defect detection. The developed model accurately identifies defects in various materials, offering a promising non-destructive testing approach.

More Related Videos

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.5K
High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

3.2K

Related Experiment Videos

Last Updated: Sep 12, 2025

Ovarian Cancer Detection Using Photoacoustic Flow Cytometry
09:18

Ovarian Cancer Detection Using Photoacoustic Flow Cytometry

Published on: January 17, 2020

6.1K
Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.5K
High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

3.2K

Area of Science:

  • Materials Science
  • Non-Destructive Testing
  • Artificial Intelligence

Background:

  • Material defect detection is crucial for ensuring structural integrity and product quality.
  • Traditional non-destructive testing methods can be limited in sensitivity and scope.
  • Photoacoustic (PA) imaging offers a promising technique for subsurface defect visualization.

Purpose of the Study:

  • To develop a novel photoacoustic (PA) signal to image conversion based convolutional neural network (CNN) model for automated material defect detection.
  • To create a low-cost computer-aided PA triggering and acquisition device for data collection.
  • To evaluate the model's performance across different materials and evaluation strategies.

Main Methods:

  • A low-cost computer-aided PA triggering and acquisition device was developed.
  • PA signals from defected and intact materials (aluminum, iron, wood, plastic) were collected.
  • Variational mode decomposition was used for PA signal to image conversion and feature extraction.
  • A lightweight CNN was trained and tested on the converted grayscale PA images for defect classification.

Main Results:

  • The proposed CNN model achieved high accuracy in defect detection.
  • Within-class (material-dependent) evaluation yielded a mean accuracy of 0.977 (up to 1.0).
  • All-class (material-independent) evaluation resulted in a mean accuracy of 0.942 (up to 0.955).

Conclusions:

  • The developed PA signal to image conversion CNN model is effective for material defect detection.
  • The model demonstrates robust performance across diverse materials, both independently and collectively.
  • This approach offers a cost-effective and accurate solution for non-destructive material inspection.