Unmanned aerial vehicles and deep learning for assessment of anthropogenic marine debris on beaches on an island in a semi-enclosed sea in Japan

作者：Kosuke Takaya, Atsuki Shibata, Yuji Mizuno, Takeshi Ise · 发表于：Environmental Research Communications · 年份：2021 · DOI：10.1088/2515-7620/ac473b · 被引用次数：23 · 研究领域：Microplastics and Plastic Pollution、Recycling and Waste Management Techniques

Abstract The increasing prevalence of marine debris is a global problem, and urgent action for amelioration is needed. Identifying hotspots where marine debris accumulates will enable effective control; however, knowledge on the location of accumulation hotspots remains incomplete. In particular, marine debris accumulation on beaches is a concern. Surveys of beaches require intensive human effort, and survey methods are not standardized. If marine debris monitoring is conducted using a standardized method, data from different regions can be compared. With an unmanned aerial vehicle (UAV) and deep learning computational methods, monitoring a wide area at a low cost in a standardized way may be possible. In this study, we aimed to identify marine debris on beaches through deep learning using high-resolution UAV images by conducting a survey on Narugashima Island in the Seto Inland Sea of Japan. The flight altitude relative to the ground was set to 5 m, and images of a 0.81-ha area were obtained. Flight was conducted twice: before and after the beach cleaning. The combination of UAVs equipped with a zoom lens and operation at a low altitude allows for the acquisition of high resolution images of 1.1 mm/pixel. The training dataset (2970 images) was annotated by using VoTT, categorizing them into two classes: ‘anthropogenic marine debris’ and ‘natural objects.’ Using RetinaNet, marine debris was identified with an average sensitivity of 51% and a precision of 76%. In addition, the...