Fully convolutional networks per segmentació d'imatges
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/327137
Correu electrònic de l'autormiguel.angel.archillaestudiant.upc.edu
Tipus de documentTreball Final de Grau
Data2020-07-14
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-CompartirIgual 3.0 Espanya
Abstract
The technology has evolved very fast and in this last decade has started a future we don’t know for sure where it will arrive. The emergence of the machine learning has brought to our present days thoughts that were very far away at the beginning of the XXI century. The progress of the artificial intelligence has allowed the development of new intelligent machines capable to learn and improve by themselves. With the help of this technology projects like the autonomous vehicle is continually improving, allowing a user do a displacement where only needs to supervise the vehicle decisions. Moreover, using the deep learning we can design in the medicinal world a machine capable to discover a disease with only the help of a few medical images and thereby, save lives more efficiently. Having in a future, scenarios where the research and the creation of new medicines will be led by artificial intelligence processes. The objective of this project is to investigate the different techniques of image analysis, in more detail the deep learning based on semantic segmentation, to evaluate the implementation viability of a CNN in a UAS for the analysis of agricultural fields with Keras and Azure Machine Learning. Approaching typical problems as the research and the increase of the dataset, the choice of a work environment and the achievement of trainings and parameters adjustments. The obtained result is positive, raising the idea of possible solutions, but there is a big improvement gap with the research of new implementation techniques.
MatèriesNeural networks (Computer science), Machine learning, Xarxes neuronals (Informàtica), Aprenentatge automàtic
TitulacióGRAU EN ENGINYERIA TELEMÀTICA (Pla 2009)
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memoria.pdf | 3,774Mb | Visualitza/Obre |