4 citations · 5 across the 3 of their papers we have counts for
6 papers
MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture
Diogo Nunes Goncalves, Jose Marcato Junior, Pedro Zamboni +4
Multi-task learning has proven to be effective in improving the performance of correlated tasks. Most of the existing methods use a backbone to extract initial features with indepe…
Semantic Segmentation with Labeling Uncertainty and Class Imbalance
Patrik Olã Bressan, José Marcato Junior, José Augusto Correa Martins +8
Recently, methods based on Convolutional Neural Networks (CNN) achieved impressive success in semantic segmentation tasks. However, challenges such as the class imbalance and the u…
Counting and Locating High-Density Objects Using Convolutional Neural Network
Mauro dos Santos de Arruda, Lucas Prado Osco, Plabiany Rodrigo Acosta +8
This paper presents a Convolutional Neural Network (CNN) approach for counting and locating objects in high-density imagery. To the best of our knowledge, this is the first object…
A Deep Learning Approach Based on Graphs to Detect Plantation Lines
Diogo Nunes Gonçalves, Mauro dos Santos de Arruda, Hemerson Pistori +8
Deep learning-based networks are among the most prominent methods to learn linear patterns and extract this type of information from diverse imagery conditions. Here, we propose a…
A CNN Approach to Simultaneously Count Plants and Detect Plantation-Rows from UAV Imagery
Lucas Prado Osco, Mauro dos Santos de Arruda, Diogo Nunes Gonçalves +11
In this paper, we propose a novel deep learning method based on a Convolutional Neural Network (CNN) that simultaneously detects and geolocates plantation-rows while counting its p…
A Complex Network Approach for Nanoparticle Agglomeration Analysis in Nanoscale Images
Bruno Brandoli, Leonardo Scabini, Jonathan Orue +5
Complex networks have been widely used in science and technology because of their ability to represent several systems. One of these systems is found in Biochemistry, in which the…