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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Pixel-Level Fatigue Crack Segmentation in Large-Scale Images of Steel Structures Using an Encoder-Decoder Network.

Chuanzhi Dong¹, Liangding Li², Jin Yan³

¹Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This study introduces an automated U-net based framework for precise fatigue crack segmentation in steel structures. The method enhances structural integrity assessment and maintenance by overcoming limitations of manual inspections and costly non-destructive testing.

Keywords:

computer vision deep learning fatigue crack semantic segmentation steel structures

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Area of Science:

Civil Engineering
Computer Vision
Materials Science

Background:

Fatigue cracks in steel structures pose significant risks, potentially leading to catastrophic failure.
Current inspection methods like visual inspection are subjective and labor-intensive, while non-destructive testing is expensive.
Accurate fatigue crack detection is crucial for structural health monitoring, maintenance, and extending service life.

Purpose of the Study:

To develop an automated, pixel-level fatigue crack segmentation framework for large-scale steel structure images.
To address the limitations of existing manual and non-destructive testing methods for crack detection.
To improve the efficiency and accuracy of fatigue crack assessment in civil infrastructure.

Main Methods:

A modified U-net encoder-decoder network was employed for pixel-level fatigue crack segmentation.
Large-resolution images were cropped into smaller segments for efficient training and testing.
Image post-processing techniques, including opening and closing operations, were applied to refine segmentation maps.

Main Results:

The proposed U-net based method achieved acceptable accuracy in automatic fatigue crack segmentation, measured by average intersection over union (mIOU).
Comparative analysis showed superior performance of the U-net model over a Fully Convolutional Network (FCN) with ResNet34 backbone.
The U-net model demonstrated better segmentation results with fewer training epochs and a simpler architecture.

Conclusions:

The developed framework offers an effective and efficient solution for fatigue crack segmentation in large-scale steel structures.
The study provides valuable insights and recommendations for applying image-based fatigue crack detection in civil infrastructure engineering.
This automated approach supports better condition assessment, maintenance planning, and lifecycle management of existing structures.