An Empirical Study on Annoying and Illegal Behavior Detection in River Space by Deep Learning

DOI
  • YAMAWAKI Masashi
    株式会社建設技術研究所 本社国土文化研究所インテリジェンスサービスプラットフォーム
  • URUSHIDANI Kouki
    株式会社建設技術研究所 九州支社情報・防災室
  • NAKATA Takashi
    株式会社建設技術研究所 東京本社情報・電気通信部
  • HOKKYO Hiromu
    株式会社建設技術研究所 本社国土文化研究所インテリジェンスサービスプラットフォーム
  • TANAKA Yuuta
    国土交通省 近畿地方整備局 企画部企画課
  • YOSHII Takahiro
    国土交通省 近畿地方整備局 淀川河川事務所
  • MURAKAMI Kouhei
    国土交通省 都市局 都市計画課

Bibliographic Information

Other Title
  • 深層学習による河川空間内の迷惑・不法行為検知に関する実証的研究

Abstract

<p>River space is a valuable open space where we can enjoy the richness of nature, water culture, and waterfront scenery. On the other hand, because it is a familiar space, there are many annoying and illegal behavior s such as illegal dumping of garbage and driving cars in the river channel. In addition, tasks such as restoration of the current situation and calling for attention are a burden on river management.</p><p>In this study, we are developing annoying and illegal behavior detection technology using CNN(Convolutional Neural Network) of the deep learning model for the purpose of improving river management and labor saving. In this paper, we conducted a demonstration experiment with a camera video analysis and warning system that implements the technology. The target locations are four locations in Yodo River where annoying and illegal behaviors occur frequently. As a result, the actual behavior was detected with high accuracy. And we showed the possibility that the warning based on the detection result contributes to the reduction of behavior.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390298124265444480
  • DOI
    10.11532/jsceiii.4.3_163
  • ISSN
    24359262
  • Text Lang
    ja
  • Data Source
    • JaLC
  • Abstract License Flag
    Disallowed

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