Improving Post-Earthquake Crack Detection using Semi-Synthetic Generated Images
This work addresses the challenge of rapid safety evaluation after earthquakes for experts, though it is incremental as it builds on existing data augmentation methods.
The study tackled the problem of limited labeled datasets for post-earthquake crack detection by introducing a semi-synthetic image generation technique using parametric meta-annotations on 3D models, resulting in a crack detection system that outperformed one trained only on real images.
Following an earthquake, it is vital to quickly evaluate the safety of the impacted areas. Damage detection systems, powered by computer vision and deep learning, can assist experts in this endeavor. However, the lack of extensive, labeled datasets poses a challenge to the development of these systems. In this study, we introduce a technique for generating semi-synthetic images to be used as data augmentation during the training of a damage detection system. We specifically aim to generate images of cracks, which are a prevalent and indicative form of damage. The central concept is to employ parametric meta-annotations to guide the process of generating cracks on 3D models of real-word structures. The governing parameters of these meta-annotations can be adjusted iteratively to yield images that are optimally suited for improving detectors' performance. Comparative evaluations demonstrated that a crack detection system trained with a combination of real and semi-synthetic images outperforms a system trained on real images alone.