REVISTA DYNA NEW TECHNOLOGIES

Key words:

Deep Learning, Residual Networks, Monocular Depth Estimation, image training, dataset, Aprendizaje Profundo, Redes Residuales, Estimación de Profundidad Monocular

Article type:

ARTICULO DE INVESTIGACION / RESEARCH ARTICLE

Section:

RESEARCH ARTICLES

ABSTRACT:
Presently, depth estimation research has focused on deep learning, mainly on monocular depth estimation, because this technique has proven to be an excellent alternative for methods that use expensive sensors or require high computational consumption, providing higher performance and accuracy. Despite there being a large number of works, most have focused on indoor and urban datasets. Due to this, we review the variants of the residual networks being trained with images of natural environments with a high presence of vegetation.
We proposed a new dataset specialized in natural environments using the AirSim simulation to evaluate the variants of the ResNet and ResNeXt families and verify that these can be used to detect leaves and thin branches with their corresponding depth. We conclude that a deeper residual neural network is not always the best option for monocular depth estimation. It is necessary to consider the type of network architecture, computing resources available, and the content and size of the data set.

Keywords: Deep Learning, Residual Networks, Monocular Depth Estimation