Visualizing corrosion in automobiles using generative adversarial networks

Abstract

In this contribution, we classify the state of corrosion of cars in images as none, mild, moderate, and severe. We use generative adversarial networks to help to transfigure non-corroded car images to the other classes. In other words, the model ingests an image with a car having no corrosion and generates an image of this car at any of either mild, moderate, or severe corrosion levels. We proposed an approach that is able to handle the particularities of this application. For example, we had to work with only several hundred images as opposed to the many thousands to million images commonly found in many computer vision problems. Our data is highly unbalanced with many more images of cars with no corrosion dominating the cars with any level of corrosion. Additionally, the data is poorly labeled, as classification is highly subjective. Despite the challenges, results indicate that our generative adversarial networks can be trained with relative accuracy given limitations on the data set. Obviously, these results show that the performance of the model depends on how well the training set represents the particular target corrosion level.