Conference paper Open Access
Centroid Loss for Weakly-Supervised Semantic Segmentation in a Quality-Control Application
Kai Yao;
Alberto Ortiz;
Francisco Bonnin-Pascual
Process automation is enabling a level of accuracy and productivity that goes beyond human ability, and one critical area where automation is making a big difference is quality control. In this paper, we describe a semantic segmentation solution aiming at detecting the presence of quality control elements in surgery toolboxes prepared by the sterilization unit of a hospital. In order to reduce the time required to prepare pixel-level ground truth, this work focuses on the use of weakly-supervised annotations (scribbles). Moreover, our solution integrates a clustering approach into a semantic segmentation network, thereby reducing the negative effects caused by weakly-supervised annotations. The paper describes the design process and reports on the results obtained.
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ETFA2020_Yao_.pdf
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- Publication date:
- September 9, 2020
- DOI:
-
- Keyword(s):
- semantic segmentation quality control
- Grants:
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European Commission:
- ROBINS - Robotics Technology for Inspection of Ships (779776)
- Meeting:
- IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria, 8-11 September 2020
- License (for files):
- Creative Commons Attribution 4.0 International