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Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight

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Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks.

Anastasiia Kyslytsyna¹, Kewen Xia¹, Artem Kislitsyn²

¹School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China.

Sensors (Basel, Switzerland)

|November 13, 2021

Summary

This summary is machine-generated.

Improved conditional generative adversarial networks (cGAN) enhance roadway crack detection. The new method accurately identifies crack shapes for better road safety analysis and monitoring.

Keywords:

attention gate conditional generative adversarial networks dashboard images dataset road crack detection

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Area of Science:

Civil Engineering
Computer Vision
Artificial Intelligence

Background:

Road surface monitoring is crucial for safety and maintenance.
Conditional generative adversarial networks (cGAN) show promise for crack detection but struggle with real-world image variations and accurate shape detection.

Purpose of the Study:

To propose an improved cGAN with attention gate (ICGA) for more accurate roadway surface crack detection.
To enhance the mathematical analysis of detected cracks by improving shape accuracy.

Main Methods:

Developed a multi-level model with independent stages for noise removal and crack identification.
Integrated two attention gates into a U-net architecture within the pix2pix framework.
Created a new dataset by removing non-road elements as noise in the first stage.

Main Results:

The proposed ICGA method demonstrated superior performance compared to existing state-of-the-art methods.
Achieved more accurate detection of crack shapes, crucial for subsequent mathematical analysis.
Successfully focused on crack redistribution, ignoring auxiliary image elements.

Conclusions:

The ICGA method offers a significant advancement in roadway crack detection accuracy.
This technique improves the reliability of road surface condition assessments.
The enhanced segmentation capabilities are vital for road maintenance and safety initiatives.