MAGI: Multistream Aerial Segmentation of Ground Images with Small-Scale Drones

In recent years, small-scale drones have been used in heterogeneous tasks, such as border control, precision agriculture, and search and rescue. This is mainly due to their small size that allows for easy deployment, their low cost, and their increasing computing capability. The latter aspect allows for researchers and industries to develop complex machine- and deep-learning algorithms for several challenging tasks, such as object classification, object detection, and segmentation. Focusing on segmentation, this paper proposes a novel deep-learning model for semantic segmentation. The model follows a fully convolutional multistream approach to perform segmentation on different image scales. Several streams perform convolutions by exploiting kernels of different sizes, making segmentation tasks robust to flight altitude changes. Extensive experiments were performed on the UAV Mosaicking and Change Detection (UMCD) dataset, highlighting the effectiveness of the proposed method.

Automated summary

In recent years, small-scale drones have been widely used in various tasks due to their small size, easy deployment, low cost, and increased computing capability. This paper proposes a novel deep-learning model for semantic segmentation, utilizing a multi-stream convolution approach to enhance robustness to changes in flight altitude at different image scales. Extensive experiments on the UMCD dataset demonstrate the effectiveness of the proposed method.