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Weakly-supervised segmentation with ensemble explainable AI: A comprehensive evaluation on crack detection.

Fupeng Wei¹, Yibo Jiao¹, Zhongmin Huangfu¹

¹School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.

The Review of Scientific Instruments

|April 18, 2025

Summary

This summary is machine-generated.

This study enhances crack detection for structural health monitoring by integrating multiple Explainable Artificial Intelligence (XAI) methods. The approach improves accuracy in weakly supervised crack segmentation, reducing manual annotation efforts.

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Area of Science:

Structural Engineering
Artificial Intelligence
Computer Vision

Background:

Surface crack detection is vital for structural health monitoring.
Deep learning methods for crack detection require extensive pixel-level annotation, increasing costs and time.
Existing Explainable Artificial Intelligence (XAI) methods struggle with the complex characteristics of cracks.

Purpose of the Study:

To address the limitations of current XAI methods in crack detection.
To improve the accuracy of pseudo-labeling for crack segmentation.
To develop an integrated XAI approach for more effective structural health monitoring.

Main Methods:

Examined various XAI strategies through extensive experimentation.
Synthesized advantages of different XAI methods to reduce uncertainty errors.
Formulated and implemented integration strategies for multiple XAI algorithms across two datasets.

Main Results:

The proposed integration of XAI strategies effectively mitigated uncertainty errors.
Discrepancies across distinct XAI algorithms were enhanced.
The method demonstrated superior performance in generating basic annotations for weakly supervised crack segmentation.

Conclusions:

Integrating multiple XAI strategies offers a more robust solution for crack detection.
The developed approach significantly improves the efficiency and accuracy of crack annotation.
This work advances the field of structural health monitoring through enhanced AI-driven analysis.