Counting and Locating High-Density Objects Using Convolutional Neural Network

no code implementations • 8 Feb 2021 • Mauro dos Santos de Arruda, Lucas Prado Osco, Plabiany Rodrigo Acosta, Diogo Nunes Gonçalves, José Marcato Junior, Ana Paula Marques Ramos, Edson Takashi Matsubara, Zhipeng Luo, Jonathan Li, Jonathan de Andrade Silva, Wesley Nunes Gonçalves

For the tree dataset, our method returned a mean absolute error (MAE) of 2. 05, a root-mean-squared error (RMSE) of 2. 87 and a coefficient of determination (R$^2$) of 0. 986.

Object Counting Vocal Bursts Intensity Prediction

A Deep Learning Approach Based on Graphs to Detect Plantation Lines

no code implementations • 5 Feb 2021 • Diogo Nunes Gonçalves, Mauro dos Santos de Arruda, Hemerson Pistori, Vanessa Jordão Marcato Fernandes, Ana Paula Marques Ramos, Danielle Elis Garcia Furuya, Lucas Prado Osco, Hongjie He, Jonathan Li, José Marcato Junior, Wesley Nunes Gonçalves

This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) for the displacement vectors between the plants.

A CNN Approach to Simultaneously Count Plants and Detect Plantation-Rows from UAV Imagery

no code implementations • 31 Dec 2020 • Lucas Prado Osco, Mauro dos Santos de Arruda, Diogo Nunes Gonçalves, Alexandre Dias, Juliana Batistoti, Mauricio de Souza, Felipe David Georges Gomes, Ana Paula Marques Ramos, Lúcio André de Castro Jorge, Veraldo Liesenberg, Jonathan Li, Lingfei Ma, José Marcato Junior, Wesley Nunes Gonçalves

In the corn plantation datasets (with both growth phases, young and mature), our approach returned a mean absolute error (MAE) of 6. 224 plants per image patch, a mean relative error (MRE) of 0. 1038, precision and recall values of 0. 856, and 0. 905, respectively, and an F-measure equal to 0. 876.