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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15800
Title: Analyzing the effect of hyperparameters in a automobile classifier based on convolutional neural networks
Authors: Laura Riveros, Elian
Galdos Chávez, José
Gutiérrez Cáceres, Juan
Keywords: Automobiles;Convolution;Image processing;Neural networks;Security systems;Activation functions;Binary classification;Convolutional networks;Convolutional neural network;Hierarchical architectures;Illumination intensity;Number of layers;Surveillance cameras;Image classification
Issue Date: 2017
Publisher: IEEE Computer Society
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017595640&doi=10.1109%2fSCCC.2016.7836010&partnerID=40&md5=0fc0280cf6fcabcc1860f121713f67e2
Abstract: In the recent years the convolutional neural network is used successfully in applications of image classification, due to its deep and hierarchical architecture. The hyper parameters of the convolutional neural networks are of great influence to obtain good results in binary classification without the need of a large number of layers. The activation function, the weights initialization and the sub sampling function are the three main hyper parameters. In the present work 27 models of convolutional neural network are trained and tested with automobile images taken from a surveillance camera. The illumination intensity of the test images are different from the training images, because they were taken from scenes of day, evening and night. We also demonstrate the influence of the mean of the images and the size of the filter kernel. The convolutional neural network model with the best result reached 95.6% of accuracy. The results of experiments show that neural networks predict successfully automobile images with varied illumination intensities overcome the techniques Haar Cascade and the Support Vector Machine. © 2016 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15800
ISBN: 9781509033393
ISSN: 15224902
Appears in Collections:Artículos de investigación

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