DOES - A multimodal dataset for supervised and unsupervised analysis of steel scrap

Abstract

DOES - Dataset of European scrap classes. Today, scrap is already an important raw material for industry. Due to the transformation to green steel, the secondary raw material scrap will become increasingly important in the coming years. With DOES a free dataset is presented, which represents common non-alloyed European scrap classes. Two important points were considered in this dataset. First, scrap oxidizes under normal external conditions and the visual appearance changes, which plays an important role in visual inspections. Therefore, DOES includes scrap images of different degrees of corrosion attack. Second, images of scrap metal (mostly scrap piles) usually have no intrinsic order. For this reason, a technique to extract many overlapping rectangles from raw images was used, which can be used to train deep learning algorithms without any disadvantage. This dataset is very suitable to develop industrial applications or to research classification algorithms. The dataset was validated by experts and through machine learning models.