Photometric Stereo-Based Defect Detection System for Steel Components Manufacturing Using a Deep Segmentation Network
dc.contributor.author | Saiz, Fátima A. | |
dc.contributor.author | Barandiaran, Iñigo | |
dc.contributor.author | Arbelaiz, Ander | |
dc.contributor.author | Graña Romay, Manuel María | |
dc.date.accessioned | 2022-02-18T19:07:01Z | |
dc.date.available | 2022-02-18T19:07:01Z | |
dc.date.issued | 2022-01-24 | |
dc.identifier.citation | Sensors 22(3) : (2022) // Article ID 882 | es_ES |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10810/55527 | |
dc.description.abstract | This paper presents an automatic system for the quality control of metallic components using a photometric stereo-based sensor and a customized semantic segmentation network. This system is designed based on interoperable modules, and allows capturing the knowledge of the operators to apply it later in automatic defect detection. A salient contribution is the compact representation of the surface information achieved by combining photometric stereo images into a RGB image that is fed to a convolutional segmentation network trained for surface defect detection. We demonstrate the advantage of this compact surface imaging representation over the use of each photometric imaging source of information in isolation. An empirical analysis of the performance of the segmentation network on imaging samples of materials with diverse surface reflectance properties is carried out, achieving Dice performance index values above 0.83 in all cases. The results support the potential of photometric stereo in conjunction with our semantic segmentation network. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | |
dc.subject | photometric stereo | es_ES |
dc.subject | quality control | es_ES |
dc.subject | deep learning | es_ES |
dc.subject | mage processing | es_ES |
dc.subject | semantic segmentation | es_ES |
dc.title | Photometric Stereo-Based Defect Detection System for Steel Components Manufacturing Using a Deep Segmentation Network | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2022-02-11T14:47:04Z | |
dc.rights.holder | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/1424-8220/22/3/882 | es_ES |
dc.identifier.doi | 10.3390/s22030882 | |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala |
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Except where otherwise noted, this item's license is described as © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).