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

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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.
Original languageEnglish
Title of host publicationComputer Vision and Pattern Analysis Across Domains
Subtitle of host publicationProceedings of the OAGM Workshop 2021
EditorsMarkus Seidl, Matthias Zeppelzauer, Peter M. Roth
PublisherVerlag der Technischen Universität Graz
ISBN (Electronic)978-3-85125-869-1
Publication statusPublished - 2022
EventOAGM Workshop 2021: Computer Vision and Pattern Analysis Across Domains - University of Applied Sciences St. Pölten, abgesagt, Austria
Duration: 24 Nov 202125 Nov 2021


ConferenceOAGM Workshop 2021


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