Publication: Lane following learning based on semantic segmentation with chroma key and image superposition
dc.affiliation.dpto | UC3M. Departamento de Informática | es |
dc.affiliation.grupoinv | UC3M. Grupo de Investigación: Laboratorio de Control, Aprendizaje y Optimización de Sistemas (CAOS) | es |
dc.contributor.author | Corrochano Jiménez, Javier | |
dc.contributor.author | Alonso Weber, Juan Manuel | |
dc.contributor.author | Sesmero Lorente, María Paz | |
dc.contributor.author | Sanchis de Miguel, María Araceli | |
dc.contributor.funder | Comunidad de Madrid | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (España) | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (España) | es |
dc.date.accessioned | 2022-05-09T10:51:36Z | |
dc.date.available | 2022-05-09T10:51:36Z | |
dc.date.issued | 2021-12 | |
dc.description.abstract | There are various techniques to approach learning in autonomous driving; however, all of them suffer from some problems. In the case of imitation learning based on artificial neural networks, the system must learn to correctly identify the elements of the environment. In some cases, it takes a lot of effort to tag the images with the proper semantics. This is also relevant given the need to have very varied scenarios to train and to thus obtain an acceptable generalization capacity. In the present work, we propose a technique for automated semantic labeling. It is based on various learning phases using image superposition combining both scenarios with chromas and real indoor scenarios. This allows the generation of augmented datasets that facilitate the learning process. Further improvements by applying noise techniques are also studied. To carry out the validation, a small-scale car model is used that learns to automatically drive on a reduced circuit. A comparison with models that do not rely on semantic segmentation is also performed. The main contribution of our proposal is the possibility of generating datasets for real indoor scenarios with automatic semantic segmentation, without the need for endless human labeling tasks. | en |
dc.description.sponsorship | This work was supported by the Spanish Government under projects PID2019-104793RBC31/ AEI/10.13039/501100011033, RTI2018-096036-B-C22/AEI/10.13039/501100011033, TRA2016- 78886-C3-1-R/AEI/10.13039/501100011033, and PEAVAUTO-CM-UC3M and by the Region of Madrid’s Excellence Program (EPUC3M17). | en |
dc.format.extent | 24 | |
dc.identifier.bibliographicCitation | Corrochano, J., Alonso-Weber, J. M., Sesmero, M. P., & Sanchis, A. (2021). Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition. In Electronics (Vol. 10, Issue 24, p. 3113). MDPI AG. | en |
dc.identifier.doi | https://doi.org/10.3390/electronics10243113 | |
dc.identifier.issn | 2079-9292 | |
dc.identifier.publicationfirstpage | 3113 | |
dc.identifier.publicationissue | 24 | |
dc.identifier.publicationlastpage | 3137 | |
dc.identifier.publicationtitle | Electronics (Switzerland) | en |
dc.identifier.publicationvolume | 10 | |
dc.identifier.uri | https://hdl.handle.net/10016/34737 | |
dc.identifier.uxxi | AR/0000030516 | |
dc.language.iso | eng | en |
dc.publisher | MDPI AG | en |
dc.relation.projectID | Comunidad de Madrid. PEAVAUTO-CM-UC3M | es |
dc.relation.projectID | Gobierno de España. PID2019-104793RBC31/ AEI/10.13039/501100011033 | es |
dc.relation.projectID | Gobierno de España. TRA2016- 78886-C3-1-R/AEI/10.13039/501100011033 | es |
dc.relation.projectID | Gobierno de España. RTI2018-096036-B-C22/AEI/10.13039/501100011033 | es |
dc.rights | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. | en |
dc.rights | Atribución 3.0 España | * |
dc.rights.accessRights | open access | en |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject.eciencia | Informática | es |
dc.subject.other | Automated driving | en |
dc.subject.other | Chroma key | en |
dc.subject.other | Feature extraction | en |
dc.subject.other | Imitation learning | en |
dc.subject.other | Lane following | en |
dc.subject.other | Noise addition | en |
dc.subject.other | Semantic segmentation | en |
dc.title | Lane following learning based on semantic segmentation with chroma key and image superposition | en |
dc.type | research article | * |
dc.type.hasVersion | VoR | * |
dspace.entity.type | Publication |
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