Authors:
Gabriel Lins Tenorio
;
Cristian E. Munoz Villalobos
;
Leonardo A. Forero Mendoza
;
Eduardo Costa da Silva
and
Wouter Caarls
Affiliation:
Electrical Engineering Department (DEE), Catholic University of Rio de Janeiro - PUC-Rio, Rio de Janeiro and Brazil
Keyword(s):
Deep Learning, Convolutional Neural Networks, Transfer Learning, Fine Tuning, Data Augmentation, Distributed Learning, Cross Validation, Remote Sensing, Vegetation Monitoring.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Theory and Methods
Abstract:
The present paper aims to train and analyze Convolutional Neural Networks (CNN or ConvNets) capable of classifying plant species of a certain region for applications in an environmental monitoring system. In order to achieve this for a limited training dataset, the samples were expanded with the use of a data generator algorithm. Next, transfer learning and fine tuning methods were applied with pre-trained networks. With the purpose of choosing the best layers to be transferred, a statistical dispersion method was proposed. Through a distributed training method, the training speed and performance for the CNN in CPUs was improved. After tuning the parameters of interest in the resulting network by the cross-validation method, the learning capacity of the network was verified. The obtained results indicate an accuracy of about 97%, which was acquired transferring the pre-trained first seven convolutional layers of the VGG-16 network to a new sixteen-layer convolutional network in which
the final training was performed. This represents an improvement over the state of the art, which had an accuracy of 91% on the same dataset.
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