Local Low-Altitudes Drone-based Riparian Waste Benchmark Dataset (LAD-RWB): A Case Study on the Asahi River Basin

Abstract

Until now, with the assistance of advanced technologies like drones and artificial intelligence (AI), the worldwide-attention-gained riparian environmental monitoring tasks recently have been gradually solved. However, there are still practical tasks that need to be solved by researchers when they are using drones and AI: How the trained AI model can be tested in practice; What kind of recognition-size can help the researchers get higher object detection accuracy; What kind of background will affect the object detection accuracy. Based on the mentioned issues, even there are several waste-based datasets that can be applied as benchmark globally (i.e., UAVVaste, UAV-BD and MJU-Waste), as yet in Japan, there is still no systematic local benchmark riparian waste-based dataset for application. Derived from the above, this paper takes the local location, the Asahi River Basin, Japan as the study site to design the experiments, and centers on the task of UAV-derived riparian waste-based object detection. The authors set up topics related to the influence of ground sample distance (GSD) and backgrounds (land-cover) on the accuracy of riparian waste-based object detection through various kinds of riparian waste-based images collected at different UAV flight altitudes (GSD) and at different sections (backgrounds) using multiple recognition sizes. In order to systematically analyze the above subjects, the authors set up different types of waste (can, cardboard, bottles and plastic bags), GSD (1.5, 2.0 and 2.5 cm/pixel), land-cover type (artificial- and natural- environment) and pixel-united recognition size (342×342, 900×600 and 5472×3648, pixel-unit) in corresponding groups. As another key point of this research, the authors applied AIGC-based model to detect the large-size objects in section-1 with over 80% detection ratio. This result proved the possibility of applying AIGC-based model in practical remote sensing tasks.