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.

This is a preprint version of a publication with DOI: https://doi.org/10.1109/ETFA46521.2020.9212160. This work is also supported by projects EU-H2020 BUGWRIGHT2 (GA 871260), PGC2018-095709-B-C21 (MCIU/AEI/FEDER, UE), and PROCOE/4/2017 (Govern Balear, 50% P.O. FEDER 2014-2020 Illes Balears).
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