1
Dept of Civil Eng, Amirkabir University of Technology
2
Civil Eng Dept, Amirkabir University of Technology
10.22060/ceej.2026.24715.8341
Abstract
Today, road Pavement Management Systems (PMS) require a transition from traditional methods to automated approaches to ensure safety and reduce maintenance costs. With the advancement of technology, including Autonomous Vehicles (AVs) and Intelligent Transportation Systems (ITS), the need for automatic detection and segmentation of asphalt pavement distress has become critical. However, developing deep learning-based models in this domain faces the critical challenge of the scarcity and imbalance of training data. This study presents a novel approach for the automated detection and segmentation of asphalt distress, aiming to assess pavement condition based on the hypothesis that generating realistic synthetic data can overcome data limitations. In the proposed method, a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was first developed to generate high-quality and diverse crack images using the Crack500 dataset, ensuring training stability and preventing mode collapse. Subsequently, a U-Net model was trained for pixel-wise segmentation on the combined dataset (real and synthetic). The primary innovation of this research lies in integrating the improved GAN architecture with a segmentation model to address overfitting and enhance model generalization across various environmental conditions. Results demonstrated that adding synthetic images significantly enhanced segmentation performance, achieving a Dice coefficient of 0.961 and an Intersection over Union (IoU) of 0.925. Furthermore, qualitative assessment indicated the model's superior capability in detecting fine and complex cracks in other public datasets. Finally, by integrating the model outputs into a Surface Condition Index (SCI), the proposed framework provides an intelligent, accurate, and cost-effective capability for assessing pavement conditions.
Golroo, A. and Sedighian-Fard, M. (2025). Automatic Assessment of Road Pavement Condition Using a Generative Adversarial Network Model with Gradient Penalty and U-Net-Based Segmentation. Amirkabir Journal of Civil Engineering, 57(9), -. doi: 10.22060/ceej.2026.24715.8341
MLA
Golroo, A. , and Sedighian-Fard, M. . "Automatic Assessment of Road Pavement Condition Using a Generative Adversarial Network Model with Gradient Penalty and U-Net-Based Segmentation", Amirkabir Journal of Civil Engineering, 57, 9, 2025, -. doi: 10.22060/ceej.2026.24715.8341
HARVARD
Golroo, A., Sedighian-Fard, M. (2025). 'Automatic Assessment of Road Pavement Condition Using a Generative Adversarial Network Model with Gradient Penalty and U-Net-Based Segmentation', Amirkabir Journal of Civil Engineering, 57(9), pp. -. doi: 10.22060/ceej.2026.24715.8341
CHICAGO
A. Golroo and M. Sedighian-Fard, "Automatic Assessment of Road Pavement Condition Using a Generative Adversarial Network Model with Gradient Penalty and U-Net-Based Segmentation," Amirkabir Journal of Civil Engineering, 57 9 (2025): -, doi: 10.22060/ceej.2026.24715.8341
VANCOUVER
Golroo, A., Sedighian-Fard, M. Automatic Assessment of Road Pavement Condition Using a Generative Adversarial Network Model with Gradient Penalty and U-Net-Based Segmentation. Amirkabir Journal of Civil Engineering, 2025; 57(9): -. doi: 10.22060/ceej.2026.24715.8341
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