Monocular Depth Estimation with Convolutional Neural Networks on Embedded Systems

Edgar Rodrigo Lopez Silva

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dc.rights.license	http://creativecommons.org/licenses/by-nc-nd/4.0	es_ES
dc.contributor	Jesús Carlos Pedraza Ortega	es_ES
dc.creator	Edgar Rodrigo Lopez Silva	es_ES
dc.date	2021-09-07
dc.date.accessioned	2022-01-31T17:49:12Z
dc.date.available	2022-01-31T17:49:12Z
dc.date.issued	2021-09-07
dc.identifier.uri	http://ri-ng.uaq.mx/handle/123456789/3397
dc.description	Monocular depth estimation is becoming a very interesting problem in computer vision to solve due to the several tasks that require as an input the spatial structure of a scene, such as 3D reconstruction, 3D object detection, localization and mapping. The most effective techniques for monocular depth estimation are based on large deep learning-based architectures that cannot be deployed on systems with limited computational resources and therefore preventing its use in application fields where the advantages of monocular cameras (i.e., low cost, small size, low weight and low-energy consumption) could also be exploited. Under this context, the research of low-latency deep learning architectures for monocular depth estimation is a very promising topic for which just a few methods have been proposed until now. In this master thesis, a very low-latency fully convolutional network is proposed. The quantitative results on the NYU-Depth V2 dataset show that the proposed method is 1.6x faster than the state-of-the art related method while also reducing the RMSE metric by 1.16%.	es_ES
dc.format	Adobe PDF	es_ES
dc.language.iso	eng	es_ES
dc.relation.requires	Si	es_ES
dc.rights	Acceso Abierto	es_ES
dc.subject	monocular	es_ES
dc.subject	depth	es_ES
dc.subject	low-latency	es_ES
dc.subject	convolutional	es_ES
dc.subject.classification	OTRAS	es_ES
dc.title	Monocular Depth Estimation with Convolutional Neural Networks on Embedded Systems	es_ES
dc.type	Tesis de maestría	es_ES
dc.creator.tid	curp	es_ES
dc.contributor.tid	curp	es_ES
dc.creator.identificador	LOSE930806HGTPLD05	es_ES
dc.contributor.identificador	PEOJ691222HSPDRS07	es_ES
dc.contributor.role	Director	es_ES
dc.degree.name	Maestría en Ciencias en Inteligencia Artificial	es_ES
dc.degree.department	Facultad de Ingeniería	es_ES
dc.degree.level	Maestría	es_ES