Simultaneous Object Detection and Distance Estimation for Indoor Autonomous Vehicles
dc.contributor.author | Azurmendi, Iker | |
dc.contributor.author | Zulueta Guerrero, Ekaitz | |
dc.contributor.author | López Guede, José Manuel ![]() | |
dc.contributor.author | González, Manuel | |
dc.date.accessioned | 2023-12-14T18:52:06Z | |
dc.date.available | 2023-12-14T18:52:06Z | |
dc.date.issued | 2023-11-21 | |
dc.identifier.citation | Electronics 12(23) : (2023) // Article ID 4719 | es_ES |
dc.identifier.issn | 2079-9292 | |
dc.identifier.uri | http://hdl.handle.net/10810/63391 | |
dc.description.abstract | Object detection is an essential and impactful technology in various fields due to its ability to automatically locate and identify objects in images or videos. In addition, object-distance estimation is a fundamental problem in 3D vision and scene perception. In this paper, we propose a simultaneous object-detection and distance-estimation algorithm based on YOLOv5 for obstacle detection in indoor autonomous vehicles. This method estimates the distances to the desired obstacles using a single monocular camera that does not require calibration. On the one hand, we train the algorithm with the KITTI dataset, which is an autonomous driving vision dataset that provides labels for object detection and distance prediction. On the other hand, we collect and label 100 images from a custom environment. Then, we apply data augmentation and transfer learning to generate a fast, accurate, and cost-effective model for the custom environment. The results show a performance of mAP0.5:0.95 of more than 75% for object detection and 0.71 m of mean absolute error in distance prediction, which are easily scalable with the labeling of a larger amount of data. Finally, we compare our method with other similar state-of-the-art approaches. | 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/4.0/ | |
dc.subject | object detection | es_ES |
dc.subject | YOLO | es_ES |
dc.subject | distance estimation | es_ES |
dc.subject | autonomous vehicles | es_ES |
dc.subject | indoor navigation | es_ES |
dc.subject | AGV | es_ES |
dc.title | Simultaneous Object Detection and Distance Estimation for Indoor Autonomous Vehicles | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2023-12-08T15:10:51Z | |
dc.rights.holder | © 2023 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/2079-9292/12/23/4719 | es_ES |
dc.identifier.doi | 10.3390/electronics12234719 | |
dc.departamentoes | Ingeniería de sistemas y automática | |
dc.departamentoeu | Sistemen ingeniaritza eta automatika |
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Except where otherwise noted, this item's license is described as © 2023 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/).