Utilizing generative AI for crack detection in the marble industry

摘要

Abstract Recently, deep learning algorithms have achieved great accuracies in multiple computer vision tasks by using considerable amounts of annotated data. In the case of marble surface crack detection, the formation of such a dataset is challenging. Currently, there are limited marble crack-related datasets, obstructing the development of robust inspection models for automated industrial quality control. To resolve this problem, we propose a generative Artificial Intelligence (AI) approach, reporting the first synthetic marble crack texture image dataset using four generative adversarial networks (GANs). Each of the final four sub-datasets includes images of different cracks on marble surfaces. The quality of the synthetic datasets is comparatively evaluated; quantitative and qualitative experimental results are provided, highlighting the most efficient generative method. Moreover, to verify the effectiveness and significance of this work, two deep learning segmentation models are used to assess the quality of the synthetic marble crack image datasets. Results indicate that different training strategies using synthetic data could boost the performance of segmentation by up to 6.55% compared to conventional data augmentation techniques. Thus, the first step towards deep learning-based automated marble crack segmentation is delivered, aiming for the simultaneous robotic resin application for healing cracks on-site, in marble-processing industrial settings.