Conditional Pavement Crack Data Generation for Selective Data Augmentation Using GANs

The adoption of Deep Learning techniques for automated crack detection is hindered by the lack of high-quality, balanced training datasets, a common issue resulting in detection bias and overfitting in model training. Data imbalances, where certain crack types predominate, cause detection bias and overfitting. This study addresses these challenges by implementing Conditional Wasserstein Generative Adversarial Networks (C-WGAN-GP) to selectively generate high-quality and diverse synthetic pavement crack images that augment underrepresented crack types. Images from Crack500 and CrackForest datasets were annotated and categorized into transverse, longitudinal, and alligator cracks. The contributions of the paper are (1) implementing an augmentation solution that selectively generates diverse and realistic pavement crack images, (2) solving mode collapse and training instability, common issues in training GAN models (3) and demonstrating that training with the GAN-augmented dataset improved classification metrics by 5% and achieved an overall accuracy of 83.33%. This approach not only mitigates data imbalance in the field, but also enhances the accuracy and capabilities of AI-driven pavement maintenance solutions