Supervised machine learning based surface inspection by synthetizing artificial defects

The preparation of labeled training data for supervised machine learning methods involves a lot of effort. Regarding surface inspection tasks, this endeavor is often not economically reasonable. In this paper, an artificial defect synthetization algorithm based on a multistep stochastic process is proposed. It adds defects to fault-free surface images, which can be used for supervised machine learning. By this means a deep convolutional neural network has been trained, achieving a detection rate of 94% of occurring real defects on the presented test surface.