Robust Crack Segmentation Using StyleGAN- Enhanced DeepLabV3 & ResNet50

Abstract

Inspection of structural cracks is critical for maintaining the safety and longevity of bridges and other infrastructure. Traditional methods for crack detection are often manual, labor-intensive, and prone to human error. Recent advances in deep learning and semantic segmentation provide a promising alternative, but obtaining high-quality annotated data remains a significant challenge. This paper introduces an enhanced approach to crack detection using deep learning, leveraging synthetic data generation and advanced semantic segmentation techniques. We propose the use of DeepLabV3 with a ResNet50 backbone, an extension of the DeepLabV3 architecture that incorporates a robust ResNet50 feature extractor to improve segmentation. Our approach involves generating synthetic crack images to address the data scarcity issue. This is achieved using the StyleGAN3 for realistic image synthesis. By integrating these synthetic datasets with the DeepLabV3+ model, we aim to boost segmentation performance beyond the capabilities of standard models. Hyperparameter tuning is performed to optimize the DeepLabV3 with ResNet50 configuration, achieving significant improvements in segmentation. We employ data augmentation techniques such as motion blur, zoom, and defocus to further refine model performance. The proposed method is evaluated against existing state-of-the-art techniques, demonstrating superior accuracy. The results indicate that our approach not only enhances the crack detection but also offers a novel application of synthetic data generation in deep learning for semantic segmentation. This research provides new insights into leveraging advanced neural networks and synthetic data for improved structural crack analysis.