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Related Experiment Video

Updated: Jul 10, 2025

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

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Published on: August 31, 2022

Tunnel Crack Detection Method and Crack Image Processing Algorithm Based on Improved Retinex and Deep Learning.

Jie Wu¹, Xiaoqian Zhang²

¹School of Defense, Xi'an Technological University, Xi'an 710021, China.

Sensors (Basel, Switzerland)

|November 25, 2023

Summary

This summary is machine-generated.

This study introduces an improved Retinex and deep learning method for efficient tunnel crack detection. The technique enhances image contrast and uses a VGG19 model for accurate crack measurement, improving tunnel safety.

Keywords:

crack segmentation deep learning multi-scale Retinex decomposition tunnel cracks

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Area of Science:

Civil Engineering
Computer Vision
Image Processing

Background:

Tunnel structural integrity is critical, with cracks posing significant risks of damage and collapse.
Efficient and accurate detection of tunnel cracks is a key research area for ensuring safety.

Purpose of the Study:

To propose an efficient tunnel crack detection method using improved Retinex and deep learning.
To enhance tunnel crack image contrast and accurately segment crack features.

Main Methods:

Utilized an image enhancement algorithm with multi-scale Retinex decomposition and central filtering.
Employed an improved VGG19 deep learning model for crack segmentation.
Applied the Zhang-Suen thinning method to obtain crack skeleton maps for length and width measurement.

Main Results:

The proposed method achieved high accuracy in crack detection, with maximum deviations of approximately 5 mm for length and 0.8 mm for width.
Demonstrated a shorter detection time compared to existing methods.
Verified the feasibility and effectiveness through experimental validation.

Conclusions:

The developed method offers a robust solution for efficient and accurate tunnel crack detection.
Provides a strong basis for tunnel health evaluation and proactive maintenance.
Contributes to enhanced tunnel safety and longevity.