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 counting and locating method based on a feature map enhancement and a Multi-Stage Refinement of the confidence map. The proposed method was evaluated in two counting datasets: tree and car. 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 (R2) of 0.986. For the car dataset (CARPK and PUCPR+), our method was superior to state-of-the-art methods. In the these datasets, our approach achieved an MAE of 4.45 and 3.16, an RMSE of 6.18 and 4.39, and an R2 of 0.975 and 0.999, respectively. The proposed method is suitable for dealing with high object-density, returning a state-of-the-art performance for counting and locating objects.
@article{arxiv.2102.04366,
title = {Counting and Locating High-Density Objects Using Convolutional Neural Network},
author = {Mauro dos Santos de Arruda and Lucas Prado Osco and Plabiany Rodrigo Acosta and Diogo Nunes Gonçalves and José Marcato Junior and Ana Paula Marques Ramos and Edson Takashi Matsubara and Zhipeng Luo and Jonathan Li and Jonathan de Andrade Silva and Wesley Nunes Gonçalves},
journal= {arXiv preprint arXiv:2102.04366},
year = {2022}
}