  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
(R$^2$) 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 R$^2$ 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.
