Research Journal of Applied Sciences
ISSN: Online 1993-6079ISSN: Print 1815-932x
Abstract
This study presents two training schemes of three deep convolutional neural network architectures applied to object recognition, based on the depth information supplied for a 3D camera. For this case, the depth information allows to make the set of training images of each network, its architecture and its characteristics, generating a dynamic recognition application by variation of the image capture point. The best scheme is selected to add a weighting layer with saturationn for obtain a final architecture that recognize objects to different distances with a 91.69% success that mean a maximum error of 8.31%.
How to cite this article:
M.Robinson Jimenez, S.Oscar Aviles and Diana M. Ovalle. Convolutional Neural Network Training for Robotic Applications in
3D Environments.
DOI: https://doi.org/10.36478/rjasci.2018.41.46
URL: https://www.makhillpublications.co/view-article/1815-932x/rjasci.2018.41.46