Implementação de redes convolucionais para a segmentação de imagens em tempo real com vistas à aplicação em robôs autônomos com dispositivos de visão de baixo custo
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Data
2018-03-16
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Universidade Federal de Goiás
Resumo
This work presents a study of convolutional networks to segment and classify images. The
purpose of this network is to eventually give more autonomy to LEIA 1 robot, using the
computer vision information in its processing. Methods such as this attempts to adapt the
visual perception system of living beings. The complexity of this task lies in not having
sufficient understanding of the biological system to model a system capable of processing
images with the same speed and efficiency as a human. To accomplish this work, two different
convolutional network architectures were validated. The first network has 13 layers, while the
second has 15 layers, and more adjustable weights than the first one. For training and
validation, a slice of Playing for Data dataset was used and adapted. The training set was
composed of 300 images, and the network was validated using 2500 patterns. For each
architecture, three training routines were performed, using the Adam, Nadam and Adamax
methods. The most relevant results used the 15-layer architecture with Adamax optimizer.
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RODRIGUES, C. A. S. P. Implementação de redes convolucionais para a segmentação de imagens em tempo real com vistas à aplicação em robôs autônomos com dispositivos de visão de baixo custo. 2018. 110 f. Dissertação (Mestrado em Engenharia Elétrica e da Computação) - Universidade Federal de Goiás, Goiânia, 2018.