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DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery

1
Computer Vision and Remote Sensing Laboratory (Bina Lab), Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA
2
Measurement Analytics Lab (MANTIS), Conrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1128; https://doi.org/10.3390/rs11091128
Received: 12 April 2019 / Revised: 7 May 2019 / Accepted: 9 May 2019 / Published: 11 May 2019
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

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. View Full-Text
Keywords: deep learning; automatic counting; UAV; real-time deep learning; automatic counting; UAV; real-time
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Rahnemoonfar, M.; Dobbs, D.; Yari, M.; Starek, M.J. DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery. Remote Sens. 2019, 11, 1128.

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