Beach pollution, particularly the accumulation of garbage on seashores in Japan, poses a significant environmental threat. To tackle this persistent issue, beach cleaning activities are necessary. However, beach cleaning is not always scheduled regularly. It depends on the availability of volunteers, resulting in situations where they struggle to clean the beach due to excessive garbage or where there is no garbage left to clean, as the beach was cleaned a short while earlier. Hence, this paper proposes an unmanned aerial vehicle drone-based monitoring system powered with a garbage detection deep learning model. This system also integrates drone technology with a beach-cleaning ground robot to create an efficient cleanup system by planning the route for garbage collection. The research focuses on detecting garbage, locating garbage in the real world, clustering detected locations, and planning paths for ground robots. The system utilizes real-time garbage detection with the YOLOv8 model and georeferencing techniques to map garbage locations accurately. The project also employs hierarchical density-based spatial clustering of application with noise (HDBSCAN) and simulated annealing for optimal route planning. Experiments conducted in simulated and real-world environments, including Hokuto no Mizukumi Seaside Park and the Kyushu Institute of Technology Sports Ground, assessed the system’s accuracy. The results revealed a high detection accuracy of 98.33% in simulations, with an average root mean square error (RMSE) offset error of 0.25 m. In contrast, real-world conditions presented more challenges, resulting in a lower accuracy of 84.26% and an average RMSE of 1.96 m.

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