The data augmentation and supplement of feature for UAV river patrol AI using generated image by Stable Diffusion

Abstract

The data in the civil engineering field is less data with much variety. Drone river patrols should fly the vast river areas and AI must detect illegal dumping, including general garbage. The patrol drones are not constantly flying and they are rarely captured by aerial images. Thus, it is even more difficult to detect temporary target such as illegal occupation. Previous study has confirmed that the addition of images taken on the ground with different angles of view to the learning data improves learning. However, the number of images is required for training even if the images are taken on the ground. In order to improve the learning of the detection model, this study verified whether the image for data augmentation and the supplement of feature which is less in dataset by generated images by Stable Diffusion.