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

• Autor(a) principal: Rodrigues, Carlos Alberto de Sousa Parente
• Data de Publicação: 2018
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFG
• dARK ID: ark:/38995/0013000001cm6
• Texto Completo: http://repositorio.bc.ufg.br/tede/handle/tede/8811
• topic: Redes neurais artificiais; Visão computacional; Segmentação de imagens; Artificial neural networks; Computer vision; Image segmentation; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

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.