EVALUATION OF AREA AND TYPES OF FLOATING MACROPLASTICS IN RIVERS DUE TO DEEP LEARNING

DOI Web Site 4 Citations 15 References Open Access
  • OTA Hiro
    東京理科大学大学院理工学研究科土木工学専攻
  • KATAOKA Tomoya
    愛媛大学大学院 理工学研究科生産環境工学専攻環境建設工学コース
  • YOSHIDA Takushi
    八千代エンジニヤリング(株) 事業統括本部
  • NIHEI Yasuo
    東京理科大学 理工学部土木工学科

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  • 深層学習に基づく河川マクロプラスチック面積算出・種類判別手法の開発

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<p> Marine plastic wastes have been mostly originated from inland, and it is important to monitor macroplastics inflow into oceans via rivers. This study aims to develop a new image processing to capture the area and types of macroplastics with deep learning. The learning data for floating debris were collected with the field test under normal and flooding conditions. CNN and YOLO were applied to find the area and types of macroplastics. The results indicated that the CNN and YOLO can capture acceptably the area and types of macroplastics in normal flow condition. It is noted that add of the learning data under flooding conditions can greatly improve the accuracy of distinguishing the types of macroplastics by YOLO.</p>

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