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Author |
Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa |
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Title  |
Fast CNN Stereo Depth Estimation through Embedded GPU Devices |
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Journal Article |
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Year |
2020 |
Publication |
Sensors 2020 |
Abbreviated Journal |
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Volume |
Vol. 2020-June |
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11 |
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pp. 1-13 |
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Keywords |
stereo matching; deep learning; embedded GPU |
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Abstract |
Current CNN-based stereo depth estimation models can barely run under real-time
constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art
evaluations usually do not consider model optimization techniques, being that it is unknown what is
the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models
on three different embedded GPU devices, with and without optimization methods, presenting
performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth
estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture
for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically
augmenting the runtime speed of current models. In our experiments, we achieve real-time inference
speed, in the range of 5–32 ms, for 1216 368 input stereo images on the Jetson TX2, Jetson Xavier,
and Jetson Nano embedded devices. |
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English |
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14248220 |
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no |
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Call Number |
cidis @ cidis @ |
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132 |
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Author |
Ricaurte P; Chilán C; Cristhian A. Aguilera; Boris X. Vintimilla; Angel D. Sappa |
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Title |
Feature Point Descriptors: Infrared and Visible Spectra |
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Journal Article |
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Year |
2014 |
Publication |
Sensors Journal |
Abbreviated Journal |
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Volume |
Vol. 14 |
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pp. 3690-3701 |
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Keywords |
cross-spectral imaging; feature point descriptors |
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Abstract |
This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given. |
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no |
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Call Number |
cidis @ cidis @ |
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28 |
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Author |
Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa |
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Title |
Fine-tuning deep convolutional networks for lepidopterous genus recognition |
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Journal Article |
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2017 |
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Lecture Notes in Computer Science |
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Vol. 10125 LNCS |
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pp. 467-475 |
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gtsi @ user @ |
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63 |
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Author |
Dennys Paillacho; F. Novillo; W. Agila.; V. Huilcapi |
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Title |
Impacto de las redes de comunicaciones en los Sistemas Robóticos de Control |
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Journal Article |
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Year |
2015 |
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Revista Politécnica |
Abbreviated Journal |
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Vol. 35 |
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pp. 97-102 |
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Keywords |
Ethernet, CAN, Swichted Ethernet, Sistemas de control en red, Ciclo de comunicación, Control de ajuste de trayectoria |
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Abstract |
El análisis de incidencia que tienen las redes de comunicaciones sobre el comportamiento de los sistemas robóticos de control en red muestra grandes dificultades cuando se quieren hacer evaluaciones de tipo analítico. Por tal razón, en este trabajo un análisis que utiliza una aproximación basada en simulación es propuesto, de manera que el comportamiento temporal y espacial de un sistema robótico de control en red pueda ser evaluado. Para tal efecto, se propone un entorno de validación mediante el cual una red de comunicaciones permita distribuir mensajes de control entre el controlador principal y los controladores remotos ubicados en cada articulación angular del robot manipulador planar. Las interacciones entre los componentes del sistema han sido modeladas mediante un sistema de capas. Dicho modelo es llevado a un entorno de simulación con la finalidad de analizar el impacto de distintos parámetros de comunicaciones (i.e. tipo de red, tasa de datos y tamaño de datos) sobre el ciclo de comunicación y el error de seguimiento de trayectoria en un sistema robótico. |
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Escuela Politécnica Nacional |
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Español |
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Español |
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Call Number |
cidis @ cidis @ |
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33 |
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