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Automatic crack segmentation using deep high-resolution representation learning.

Hanshen Chen, Yishun Su, Wei He

|October 6, 2021

Summary

This summary is machine-generated.

This study introduces HRNet, an improved deep learning model for detecting cracks in infrastructure. HRNet enhances accuracy and robustness in crack detection with fewer parameters compared to existing methods.

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Engineering
Computer Science
Artificial Intelligence

Background:

Cracks are prevalent surface defects in engineering infrastructure.
Visual crack detection is challenging due to variations in crack appearance.
Existing convolutional neural network (CNN) models may lose spatial resolution, impacting accuracy.

Purpose of the Study:

To propose an enhanced high-resolution network (HRNet) for improved crack detection.
To address the spatial resolution loss issue in traditional encoder-decoder CNN architectures.
To achieve more accurate and robust crack detection with fewer parameters.

Main Methods:

Modified the high-resolution network (HRNet) by removing initial downsampling.
Reduced the number of high-resolution representation layers.
Incorporated dilated convolution and hierarchical feature integration.

Main Results:

The proposed HRNet demonstrated superior accuracy and robustness in crack detection.
Achieved better performance than existing recent approaches.
The enhanced model requires relatively fewer parameters.

Conclusions:

The enhanced HRNet offers a more effective solution for visual crack detection in engineering infrastructures.
The network design successfully mitigates spatial resolution loss.
This approach provides a computationally efficient and accurate method for defect identification.