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

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 abundance and area of marine debris coverage were estimated. In this study, it was revealed that the combination of UAVs and deep learning enables the effective identification of marine debris. The effects of cleanup activities by citizens were able to be quantified. This method can widely be used to evaluate the effectiveness of citizen efforts toward beach cleaning and low-cost long-term monitoring.

Fresh and Recirculated Submarine Groundwater Discharge Evaluated by Geochemical Tracers and a Seepage Meter at Two Sites in the Seto Inland Sea, Japan

Submarine groundwater discharge (SGD) consists of fresh submarine groundwater discharge (FSGD) and recirculated submarine groundwater discharge (RSGD). In this study, we conducted simultaneous 25-hour time-series measurements of short-lived 222Rn and 224Ra activities at two sites with differing SGD rates in the central Seto Inland Sea of Japan to evaluate SGD rates and their constituents. At both sites, we also quantified the total SGD, FSGD, and RSGD using a seepage meter to verify the water fluxes estimated with 222Rn and 224Ra. SGD rates estimated using 222Rn and 224Ra at the site with significant SGD approximated the total SGD and RSGD measured by the seepage meter. However, SGD rates derived using 222Rn at the site with minor SGD were overestimated, since 222Rn activity at the nearshore mooring site was lower than that in the offshore area. These results suggest that the coupling of short-lived 222Rn and 224Ra is a powerful tool for quantification of FSGD and RSGD, although it is important to confirm that tracer activities in coastal areas are higher than those in offshore.