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Microcracking in Concrete

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Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Road crack segmentation using an attention residual U-Net with generative adversarial learning.

Xing Hu¹, Minghui Yao¹, Dawei Zhang¹

¹School of Optical-Electrical Information and Computer Engineering, University of Shanghai For Science and Technology, No. 516 Jungong Road, Shanghai, 200093, China.

Mathematical Biosciences and Engineering : MBE

|November 24, 2021

Summary

This study introduces an advanced road crack segmentation model using deep fully convolutional networks (FCN) and adversarial learning. The model enhances crack detection accuracy and robustness for infrastructure monitoring.

Keywords:

adversarial learning attention residual U-Net road crack segmentation

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Accurate road crack detection is crucial for infrastructure maintenance and safety.
Existing segmentation models often struggle with capturing fine details and global context.

Purpose of the Study:

To develop an end-to-end road crack segmentation model with improved accuracy and robustness.
To leverage attention mechanisms and generative adversarial learning for enhanced feature extraction.

Main Methods:

An enhanced Fully Convolutional Network (FCN) incorporating visual attention and residual modules was developed.
Generative adversarial learning was employed using a convolutional neural network discriminator to guide segmentation network training.
The model was evaluated on three public road crack datasets.

Main Results:

The proposed model demonstrated superior performance compared to state-of-the-art methods in F1 score and precision.
Mean Intersection over Union (mIoU) improved by 3%-17% over U-Net across datasets.
Segmentation results were more refined, robust, and smooth.

Conclusions:

The integration of attention mechanisms and adversarial learning significantly enhances road crack segmentation.
The proposed model offers a robust and accurate solution for automated road inspection.
This approach provides a valuable tool for pavement management systems.