[M-IS17] 地球科学としての海洋プラスチック

[MIS17-P03] 市民科学と深層学習による街中プラスチックごみ量の推定

室屋 龍之介¹、*加古 真一郎¹、松岡 大祐²、磯辺 篤彦³ (1.鹿児島大学大学院理工学研究科、2.海洋研究開発機構、3.九州大学応用力学研究所)

キーワード：街中プラスチックごみ、深層学習、市民科学、画像解析

Approximately 80% of plastic litter entering the ocean is of terrestrial origin (Eunomia, 2016). Most of these are litter related to our daily activities that was mismanaged and discharged into the ocean via rivers (Lebreton et al., 2017). Currently, however, there is no method to accurately and objectively monitor and quantify the amount of plastic litter in terrestrial areas, in particular in the city where the source of marine plastic pollution. Existing methods for monitoring plastic litter include a quantification method for beach litter that combines drones and webcams with deep learning (Kako et al., 2022, Hidaka et al., 2022), but from the perspective of safety and privacy protection, it is difficult to apply these methods to monitoring litter on land, particularly litter in the streets. In this study, we attempted to establish a method to quantify the amount of litter in the city by combining images taken by ordinary citizens using a smartphone application and deep learning, with the aim of constructing a citizen-participatory litter monitoring network. Based on visual observations, an image analysis method capable of detecting six types of litter (can, plastic bottle, plastic bag, cigarette box, cigarette butt, and mask) in the city was constructed. The results of the accuracy validation through the visual observation showed that our image processing method based on deep learning is possible to detect and quantify 6 types of litter in the city with a high precision and recall rates of more than 70%.