Semi-supervised learning with convolutional neural networks for UAV images automatic recognition
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Data
2019-09-01
Autores
Amorim, Willian Paraguassu
Tetila, Everton Castelao
Pistori, Hemerson
Papa, Joao Paulo [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Elsevier B.V.
Resumo
The annotation of large datasets is an issue whose challenge increases as the number of labeled samples available to train the classifier reduces in comparison to the amount of unlabeled data. In this context, semi-supervised learning methods aim at discovering and propagating labels to unsupervised samples, such that their correct labeling can improve the classification performance. Our proposal makes use of semi-supervised methodologies to classify an unlabeled training set that is used to train a Convolution Neural Network using different training strategies. The proposed approach is experimentally validated for soybean leaf and herbivorous pest identification using images captured by Unmanned Aerial Vehicles and can support specialists and farmers in the pest control management in soybean fields, especially when they have a limited amount of labeled samples.
Descrição
Palavras-chave
Semi-supervised learning, Convolutional Neural Networks, Fine tuning, Transfer learning
Como citar
Computers And Electronics In Agriculture. Oxford: Elsevier Sci Ltd, v. 164, 9 p., 2019.