Classification fine par réseau de neurones à convolution

Authors: Carpentier, Mathieu
Advisor: Gaudreault, JonathanGiguère, Philippe
Abstract: Artificial intelligence is a relatively recent research domain. With it, many breakthroughs were made on a number of problems that were considered very hard. Fine-grained classification is one of those problems. However, a relatively small amount of research has been done on this task even though itcould represent progress on a scientific, commercial and industrial level. In this work, we talk about applying fine-grained classification on concrete problems such as tree bark classification and mould classification in culture. We start by presenting fundamental deep learning concepts at the root of our solution. Then, we present multiple experiments made in order to try to solve the tree bark classification problem and we detail the novel dataset BarkNet 1.0 that we made for this project. With it, we were able to develop a method that obtains an accuracy of 93.88% on singlecrop in a single image, and an accuracy of 97.81% using a majority voting approach on all the images of a tree. We conclude by demonstrating the feasibility of applying our method on new problems by showing two concrete applications on which we tried our approach, industrial tree classification and mould classification.
Document Type: Mémoire de maîtrise
Issue Date: 2019
Open Access Date: 7 August 2019
Permalink: http://hdl.handle.net/20.500.11794/35835
Grantor: Université Laval
Collection:Thèses et mémoires

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