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Integrating sensor models in deep learning boosts performance: application to monocular depth estimation in warehouse automation
dc.contributor.author | Yoneyama, Ryota | |
dc.contributor.author | Durán Bosch, Angel Juan | |
dc.contributor.author | del Pobil, Angel P. | |
dc.date.accessioned | 2021-05-07T13:29:12Z | |
dc.date.available | 2021-05-07T13:29:12Z | |
dc.date.issued | 2021-02-19 | |
dc.identifier.citation | Yoneyama, Ryota; Duran, Angel J.; del Pobil, Angel P. 2021. "Integrating Sensor Models in Deep Learning Boosts Performance: Application to Monocular Depth Estimation in Warehouse Automation" Sensors 21, no. 4: 1437. https://doi.org/10.3390/s21041437 | ca_CA |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10234/193057 | |
dc.description.abstract | Deep learning is the mainstream paradigm in computer vision and machine learning, but performance is usually not as good as expected when used for applications in robot vision. The problem is that robot sensing is inherently active, and often, relevant data is scarce for many application domains. This calls for novel deep learning approaches that can offer a good performance at a lower data consumption cost. We address here monocular depth estimation in warehouse automation with new methods and three different deep architectures. Our results suggest that the incorporation of sensor models and prior knowledge relative to robotic active vision, can consistently improve the results and learning performance from fewer than usual training samples, as compared to standard data-driven deep learning. | ca_CA |
dc.format.extent | 17 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | MDPI | ca_CA |
dc.relation | UJI Robotic Intelligence Laboratory | ca_CA |
dc.relation.isPartOf | Sensors, Volume 21, Issue 4 (February-2 2021) | ca_CA |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-sa/4.0/ | * |
dc.subject | deep learning in sensing | ca_CA |
dc.subject | robot sensors | ca_CA |
dc.subject | vision/camera based sensors | ca_CA |
dc.subject | 3D sensing | ca_CA |
dc.subject | monocular depth estimation | ca_CA |
dc.subject | warehouse automation | ca_CA |
dc.subject | optic flow | ca_CA |
dc.title | Integrating sensor models in deep learning boosts performance: application to monocular depth estimation in warehouse automation | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.3390/s21041437 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://www.mdpi.com/1424-8220/21/4/1437 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | Ministerio de Ciencia e Innnovación | ca_CA |
project.funder.name | Universitat Jaume I | ca_CA |
project.funder.name | Generalitat Valenciana | ca_CA |
oaire.awardNumber | DPI2015-69041-R | ca_CA |
oaire.awardNumber | UJI-B2018-74 | ca_CA |
oaire.awardNumber | PROMETEO / 2020/034 | ca_CA |
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