Automatic counting of cattle with Faster R-CNN on UAV images - Porto, João Vitor de Andrade; Rezende, Fábio Prestes Cesar; Astolfi, Gilberto; Weber, Vanessa Aparecida de Moraes; Pache, Marcio Carneiro Brito; Pistori, Hemerson (Anais do Workshop de Visão Computacional (WVC); 2021: Anais do XVII Workshop de Visão ComputacionalDO - 10.5753/wvc.2021.18880, 2021)
It is remarkable the growth of the bovine herd in the last four decades however, the availability of areas for pasture did not follow the same trend and thus caused direct interference in the binomial quality and price of the final product. One of the ways to get around this interference is by the use of technologies to help minimize the handling costs, from the breeding in a controlled environment with the need of trained manpower in the confinement process. Thus, as opposed to the current format done manually and in restricted space, computer vision technology can mitigate the identification and counting of cattle problems using unmanned aerial vehicle (UAV). Attending to the objective outlined in this article demonstrates the use of the Faster R-CNN for counting cattle in feedlots employing aerial images, obtaining an average precision of 89.7% for the set of hyperparameters that differed most positively from the others in this experiment.