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Single Fusion Image from Collections of Fruit Views for Defect Detection and Classification

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Single Fusion Image from Collections of Fruit Views for Defect Detection and Classification

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dc.contributor.author Albiol Colomer, Antonio José es_ES
dc.contributor.author Sánchez de-Merás, Carlos Javier es_ES
dc.contributor.author Albiol Colomer, Alberto es_ES
dc.contributor.author Hinojosa, Sara es_ES
dc.date.accessioned 2023-10-24T18:01:49Z
dc.date.available 2023-10-24T18:01:49Z
dc.date.issued 2022-07 es_ES
dc.identifier.uri http://hdl.handle.net/10251/198753
dc.description.abstract [EN] Quality assessment is one of the most common processes in the agri-food industry. Typically, this task involves the analysis of multiple views of the fruit. Generally speaking, analyzing these single views is a highly time-consuming operation. Moreover, there is usually significant overlap between consecutive views, so it might be necessary to provide a mechanism to cope with the redundancy and prevent multiple counting of defect points. This paper presents a method to create surface maps of fruit from collections of views obtained when the piece is rotating. This single image map combines the information contained in the views, thus reducing the number of analysis operations and avoiding possible miscounts in the number of defects. After assigning each piece a simple geometrical model, 3D rotation between consecutive views is estimated only from the captured images, without any further need for sensors or information about the conveyor. The fact that rotation is estimated directly from the views makes this novel methodology readily usable in high throughput industrial inspection machines without any special hardware modification. As proof of this technique's usefulness, an application is shown where maps have been used as input to a CNN to classify oranges into different categories. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject 3D es_ES
dc.subject Rotation es_ES
dc.subject Mapping es_ES
dc.subject Projection es_ES
dc.subject Unwrapping es_ES
dc.subject Quality assessment es_ES
dc.subject Fruit es_ES
dc.subject.classification TEORÍA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Single Fusion Image from Collections of Fruit Views for Defect Detection and Classification es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s22145452 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Telecomunicación - Escola Tècnica Superior d'Enginyers de Telecomunicació es_ES
dc.description.bibliographicCitation Albiol Colomer, AJ.; Sánchez De-Merás, CJ.; Albiol Colomer, A.; Hinojosa, S. (2022). Single Fusion Image from Collections of Fruit Views for Defect Detection and Classification. Sensors. 22(14):1-14. https://doi.org/10.3390/s22145452 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/s22145452 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 14 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 22 es_ES
dc.description.issue 14 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 35891127 es_ES
dc.identifier.pmcid PMC9323781 es_ES
dc.relation.pasarela S\471013 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
upv.costeAPC 2200 es_ES


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