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Related Experiment Video

Updated: Jan 18, 2026

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

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Published on: September 29, 2019

A Unified Preprocessing Pipeline for Noise-Resilient Crack Segmentation in Leaky Infrastructure Surfaces.

Jae-Jun Shin¹, Jeongho Cho¹

¹Department of Electrical Engineering, Soonchunhyang University, Asan 31538, Republic of Korea.

Sensors (Basel, Switzerland)

|September 13, 2025

Summary

This study introduces a novel preprocessing pipeline to enhance crack detection in wet environments. The method significantly improves accuracy and robustness against noise and surface irregularities.

Keywords:

crack segmentation deep learning image preprocessing infrastructure inspection wet cracks

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Published on: January 6, 2023

Area of Science:

Civil Engineering
Materials Science
Computer Vision

Background:

Wet cracks present visual distortions from contamination and corrosion, degrading sensor-based detection.
Nonlinear crack propagation in moist conditions complicates distinction from background noise like stains and low contrast.

Purpose of the Study:

To propose a robust segmentation framework with a dedicated preprocessing pipeline for enhanced crack detection in adverse conditions.
To improve the accuracy and reliability of vision-based crack detection systems in real-world infrastructure inspections.

Main Methods:

A preprocessing pipeline incorporating adaptive thresholding, morphological operations, and connected component analysis.
Contrast enhancement techniques including histogram stretching and contrast limited adaptive histogram equalization.
A background fusion step to emphasize crack features while preserving surface texture.

Main Results:

The proposed method significantly enhances segmentation performance under challenging conditions.
Achieved a precision of 97.5% with strong robustness against moisture, reflections, and surface irregularities.
Demonstrated substantial improvement in accuracy and reliability for infrastructure inspection.

Conclusions:

Targeted preprocessing is crucial for overcoming limitations in current crack detection systems.
The developed framework offers a reliable solution for detecting cracks in visually complex and moist environments.
This approach can substantially enhance the accuracy and reliability of crack detection systems in real-world infrastructure inspection scenarios.