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Related Experiment Video

Updated: Oct 20, 2025

Quantifying Intermembrane Distances with Serial Image Dilations

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Published on: September 28, 2018

Crack Length Measurement Using Convolutional Neural Networks and Image Processing.

Yingtao Yuan^1,2, Zhendong Ge^1,2, Xin Su^1,2

¹School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China.

Sensors (Basel, Switzerland)

|September 10, 2021

Summary

This summary is machine-generated.

This study introduces a new framework using convolutional neural networks (CNN) and digital image processing for accurate fatigue crack detection and length measurement, improving structural safety monitoring.

Keywords:

convolutional neural network crack length fatigue crack detection image processing

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Area of Science:

Engineering
Materials Science
Computer Vision

Background:

Fatigue failure poses a significant risk to engineering structures.
Manual inspection for fatigue cracks is time-consuming and subjective.
Existing vision-based methods struggle with crack detection amidst noise and precise tip localization.

Purpose of the Study:

To develop an automated framework for monitoring fatigue crack propagation length.
To enhance the accuracy and robustness of crack detection and tip localization.

Main Methods:

Utilized convolutional neural networks (CNN) for reliable crack detection, distinguishing them from scratches and edges.
Developed a crack tip detection algorithm for precise localization.
Integrated digital image processing techniques for crack length calculation.

Main Results:

The proposed framework accurately detected fatigue cracks even with interfering crack-like noises.
The crack tip detection algorithm precisely located crack tips.
Submillimeter accuracy was achieved in measuring crack propagation length.

Conclusions:

The novel CNN-based framework offers a robust and accurate solution for fatigue crack monitoring.
This approach significantly improves upon traditional methods for assessing structural integrity.