Counting and Locating High-Density Objects Using Convolutional Neural Network
This research provides an improved method for accurately counting and locating objects in crowded images, which is beneficial for applications requiring precise object enumeration in dense environments.
This paper addresses the problem of counting and locating objects in high-density imagery using a Convolutional Neural Network. The method achieved a mean absolute error (MAE) of 2.05 for tree counting and MAEs of 4.45 and 3.16 for car counting on two datasets, outperforming state-of-the-art methods.
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.