A study on robust feature representations for grain density estimates in austenitic steel

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung


Modern material sciences and manufacturing techniques allow us to create alloys that help shape our way of living; from jet turbines that withstand extreme stresses to railroad tracks that retain their intended shape. It is therefore an important aspect of quality control to estimate the microstructural properties of steel during and after the manufacturing process, as these microstructures determine the mechanical properties of steel. This estimation has for a long time been a labor intensive and non-trivial task which requires years of expertise.
We show that modern deep neural networks can be used to estimate the grain density of austenitic steel, while also applying a visualization technique adapted to our task to allow for the visual inspection of why certain decisions were made. We compare classification and regression models for this specific task, and show that the learned feature representations are vastly different, which might have implications for other tasks that can be solved via discretization into a classification problem or treating it as an estimation of a continuous variable.
TitelComputer Vision and Pattern Analysis Across Domains
UntertitelProceedings of the OAGM Workshop 2021
Redakteure/-innenMarkus Seidl, Matthias Zeppelzauer, Peter M. Roth
Herausgeber (Verlag)Verlag der Technischen Universität Graz
ISBN (elektronisch)978-3-85125-869-1
PublikationsstatusVeröffentlicht - 2022
Veranstaltung44th OAGM Workshop 2021: Computer Vision and Pattern Analysis Across Domains: ÖAGM 2021 - University of Applied Sciences St. Pölten, abgesagt, Österreich
Dauer: 24 Nov. 202125 Nov. 2021


Konferenz44th OAGM Workshop 2021: Computer Vision and Pattern Analysis Across Domains

Dieses zitieren