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Shahrestani, Afshin

University of British Columbia

Pavement crack detection and treatment is one of the vital tasks in road infrastructure management. Failure to do so in a proactive manner increases maintenance costs and results in a drop in the structural integrity of the pavement. Deep learning techniques promise to automate pavement crack detection, aiding proactive highway maintenance. This approach reduces costs, time, and human bias in defect analysis. One barrier to large-scale adoption of deep learning techniques in this area is the scarcity of high-quality, and balanced training datasets. Inherent data imbalances, where certain crack types predominate over others, lead to detection bias and overfitting in machine learning models. This is exacerbated by variations in crack types, skewing the model training towards more frequently occurring cracks, thereby diminishing efficacy in identifying less common types of cracks. Data augmentation is one of the possible solutions to these issues. Mode Collapse, training instability, and lack of agency in data generation were identified as the main challenges associated with previous pavement crack augmentation methods. To overcome the challenges, the thesis implements two Generative Adversarial Network (GAN) architectures based on the models Wasserstein GAN (WGAN), and Conditional WGAN (C-WGAN) developed in previous literature. These models aim to remedy the shortcomings of previous data augmentation methods used in pavement crack analysis. The research utilizes annotated images from the Crack500 and CrackForest datasets, categorized into transverse, longitudinal, block, and alligator crack types. These images are used as the training data for both the GANs and a baseline classifier, aimed at measuring the impact of synthetic crack images in augmented datasets. The findings demonstrate the effectiveness of WGAN and C-WGAN in addressing the limitations of previous augmentation methods, generating high-quality, diverse synthetic images. The GAN-augmented models achieved an average classification score improvement of 5\% over the baseline. C-WGAN offered the advantage of user-specific image generation. Both models proved to be effective data augmentation tools for pavement crack datasets, each with distinct advantages and potential trade-offs. This research contributes to the field by providing robust GAN-based solutions for enhancing pavement crack detection and classification.

Text

2024-10-01

Attribution-NoDerivatives 4.0 International

http://hdl.handle.net/2429/89312

Civil Engineering

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nd/4.0/