Abstract
In this paper we provide insights and methods for using image segmentation for the purposes of automatic corrosion damage detection. Automatic image analysis is needed in order to process all data retrieved from drone-driven industrial inspections. To this end we provide three main contributions. First, 608 images with corrosion damages are instance-wise annotated with binary segmentation masks to construct a dataset. Second, a novel, two-stage data augmentation scheme is developed and empirically shown to significantly reduce overfitting. Finally, Mask R-CNN and PSPNet are evaluated on the corrosion dataset using this and other data augmentation methods. With 77.5% and 73.2% mean intersection over union (mIoU) for Mask R-CNN and PSPNet, respectively, the results are very promising. It is concluded that image segmentation can aid automating industrial inspections of steel constructions in the future, and that instance segmentation is likely more useful than semantic segmentation due to its applications to a wider range of use-cases.
