Indention mark segmentation data

Zenodo (CERN European Organization for Nuclear Research)

Abstract

This dataset includes 1120 electron microscopy images containing an indentation mark and their ground-truth masks to detect the indent marks. Both images and masks are in png format and have a (W=1024, H=768) size. The dataset can be used for semantic segmentation. The images were taken from different scanning electron microscopes (SEMs) and have different degrees of brightness and contrast. Indentation marks are used to evaluate the mechanical properties (hardness) of materials. This Github repository uses the attached data to segment away the indentation mark and detect lines, using OpenCV, on its sides. Acknowledgement: The authors gratefully acknowledge the German Federal Ministry of Education and Research (BMBF) and the government of Nordrhein-Westfalen and the Hessian ministry for supporting this work/project as part of the NHR funding. This work was supported by the German research foundation (DFG) within the Collaborative Research Centre SFB 1394 ‘‘Structural and Chemical Atomic Complexity—From Defect Phase Diagrams to Materials Properties” (Project ID 409476157), project A05 and C02. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (Grant Agreement No. 852096 FunBlocks).

Authors 6

1. Raheleh Hadian Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

2. M. Freund Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

3. J. Panthel Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

4. Christina Gasper Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

5. Isabel Gao Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

6. Sandra Korte‐Kerzel Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University