Automated sewer defects detection has become an important trend for better management and maintenance of urban sewer systems. Deep learning technology has developed rapidly and offers an innovative solution for automated detection in engineering applications. However, insufficient data and unbalanced samples have proposed a big challenge to deep learning model training. This study adopts the state-of-the-art Style-based Generative Adversarial Networks (StyleGANs) model and compares the performances of its two variants in producing high-quality synthetic sewer defects images. Seven well-known CNN models are further fine-tuned and trained using the synthetic images for automated sewer defects detection to examine the effects of StyleGANs on augmenting the detection performance. Results show that both StyleGANs are efficient in producing high-quality images with various styles and high-level details for multiple types of sewer defects. Specifically, the StyleGAN2-Adaptive Discriminator Augmentation (StyleGAN2-ADA) with the aid of Freeze Discriminator (Freeze-D) yields the best model performance. Among the adopted CNN classifiers, Inception_v3 achieves the highest detection accuracy. The mean detection accuracy is 94% (with a specific accuracy of 99.7%, 97%, 95.3% and 84% for tree root, residential wall, disjoint and obstacle, respectively) and confirms the reliability of the StyleGANs’ performance. The study shows that StyleGANs provide a promising method to alleviate the limited and uneven dataset problem and can improve the deep learning model performance.
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