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Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

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A Deep Learning-Based Watershed Feature Fusion Approach for Tunnel Crack Segmentation in Complex Backgrounds.

Haozheng Wang^1,2, Qiang Wang^1,2,3, Weikang Zhang^1,2

¹Zhejiang Scientific Research Institute of Transport, Hangzhou 311305, China.

Materials (Basel, Switzerland)

|January 11, 2025

Summary

This summary is machine-generated.

This study introduces an automated method for crack detection in highway tunnels, improving accuracy and efficiency. The optimized deep learning model significantly enhances structural defect analysis, addressing limitations of manual inspection.

Keywords:

deep learning image recognition lining cracks machine vision tunnel detection

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Area of Science:

Civil Engineering
Computer Vision
Artificial Intelligence

Background:

Highway tunnel structural defects, especially cracks, increase annually, overwhelming traditional manual inspection methods.
Rapid tunnel network expansion necessitates advanced automated inspection solutions.
Existing machine vision and deep learning methods face challenges in tunnel crack detection due to complex backgrounds and data labeling.

Purpose of the Study:

To develop an automated labeling and optimization algorithm for crack sample sets in highway tunnels.
To enhance the performance of deep learning-based crack segmentation networks.
To provide an efficient and accurate solution for identifying structural defects in tunnels.

Main Methods:

Utilized crack features and the watershed algorithm for efficient automated segmentation with minimal human input.
Optimized deep learning crack segmentation networks by analyzing various network depths and residual structure configurations.
Incorporated axis extraction and watershed filling algorithms to refine segmentation outcomes.

Main Results:

Achieved a crack segmentation accuracy of 98.78% under diverse lining surface conditions and interference factors.
Obtained an Intersection over Union (IoU) of 72.41% for crack segmentation.
Demonstrated a robust solution for crack segmentation in tunnels with complex backgrounds.

Conclusions:

The proposed automatic labeling and deep learning optimization algorithm effectively addresses limitations in tunnel crack detection.
The enhanced segmentation accuracy and IoU provide a reliable tool for infrastructure monitoring.
This approach offers a significant advancement for intelligent crack segmentation in civil engineering applications.