Towards Robust Ferrous Scrap Material Classification with Deep Learning and Conformal Prediction

arXiv:2404.13002v1 Announce Type: cross
Abstract: In the steel production domain, recycling ferrous scrap is essential for environmental and economic sustainability, as it reduces both energy consumption and greenhouse gas emissions. However, the classification of scrap materials poses a significant challenge, requiring advancements in automation technology. Additionally, building trust among human operators is a major obstacle. Traditional approaches often fail to quantify uncertainty and lack clarity in model decision-making, which complicates acceptance. In this article, we describe how conformal prediction can …

abstract arxiv automation automation technology building challenge classification consumption cs.cv cs.lg deep learning domain economic emissions energy environmental greenhouse however material materials prediction production recycling robust sustainability technology trust type