%0 Journal Article
%T DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery
%A Dugan Dobbs
%A Maryam Rahnemoonfar
%A Masoud Yari
%A Michael J. Starek
%J Remote Sensing | An Open Access Journal from MDPI
%D 2019
%R https://doi.org/10.3390/rs11091128
%X Recent deep-learning counting techniques revolve around two distinct features of data¡ªsparse data, which favors detection networks, or dense data where density map networks are used. Both techniques fail to address a third scenario, where dense objects are sparsely located. Raw aerial images represent sparse distributions of data in most situations. To address this issue, we propose a novel and exceedingly portable end-to-end model, DisCountNet, and an example dataset to test it on. DisCountNet is a two-stage network that uses theories from both detection and heat-map networks to provide a simple yet powerful design. The first stage, DiscNet, operates on the theory of coarse detection, but does so by converting a rich and high-resolution image into a sparse representation where only important information is encoded. Following this, CountNet operates on the dense regions of the sparse matrix to generate a density map, which provides fine locations and count predictions on densities of objects. Comparing the proposed network to current state-of-the-art networks, we find that we can maintain competitive performance while using a fraction of the computational complexity, resulting in a real-time solution. View Full-Tex
%U https://www.mdpi.com/2072-4292/11/9/1128