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Article

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.
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)
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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MDPI and ACS Style

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. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091128

AMA Style

Rahnemoonfar M, Dobbs D, Yari M, Starek MJ. DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery. Remote Sensing. 2019; 11(9):1128. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091128

Chicago/Turabian Style

Rahnemoonfar, Maryam, Dugan Dobbs, Masoud Yari, and Michael J. Starek 2019. "DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery" Remote Sensing 11, no. 9: 1128. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091128

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