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Battery-Powered Wild Animal Detection Nodes with Deep Learning Hiroshi SAITO Tatsuki OTAKE Hayato KATO Masayuki TOKUTAKE Shogo SEMBA Yoichi TOMIOKA Yukihide KOHIRA Publication IEICE TRANSACTIONS on Communications Vol.E103-B No.12 pp.1394-1402 Publication Date: 2020/12/01 Publicized: 2020/07/01 Online ISSN: 1745-1345 DOI: 10.1587/transcom.2020SEP0004 Type of Manuscript: Special Section PAPER (Special Section on IoT Sensor Networks and Mobile Intelligence) Category: Keyword: wild animal detection, deep learning, camera-trap, micro-computer boards, and power saving, Full Text: PDF(2.2MB)>>
Summary: Since wild animals are causing more accidents and damages, it is important to safely detect them as early as possible. In this paper, we propose two battery-powered wild animal detection nodes based on deep learning that can automatically detect wild animals; the detection information is notified to the people concerned immediately. To use the proposed nodes outdoors where power is not available, we devise power saving techniques for the proposed nodes. For example, deep learning is used to save power by avoiding operations when wild animals are not detected. We evaluate the operation time and the power consumption of the proposed nodes. Then, we evaluate the energy consumption of the proposed nodes. Also, we evaluate the detection range of the proposed nodes, the accuracy of deep learning, and the success rate of communication through field tests to demonstrate that the proposed nodes can be used to detect wild animals outdoors. | |||||
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