Rahnemoonfar, MaryamDobbs, DuganYari, MasoudStarek, Michael J.2021-10-272021-10-272019-05-11Rahnemoonfar, M., Dobbs, D., Yari, M. and Starek, M.J., 2019. DisCountNet: Discriminating and counting network for real-time counting and localization of sparse objects in high-resolution UAV imagery. Remote Sensing, 11(9), p.1128.Rahnemoonfar, M., Dobbs, D., Yari, M. and Starek, M.J., 2019. DisCountNet: Discriminating and counting network for real-time counting and localization of sparse objects in high-resolution UAV imagery. Remote Sensing, 11(9), p.1128.https://hdl.handle.net/1969.6/89877Recent deep-learning counting techniques revolve around two distinct features of data—sparse 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.Recent deep-learning counting techniques revolve around two distinct features of data—sparse 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.en-USAttribution 4.0 InternationalAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by/4.0/deep-learningautomatic countinguavreal-timedeep-learningautomatic countinguavreal-timeArticlehttps://doi.org/10.3390/rs11091128