MAR20： A benchmark for military aircraft recognition in remote sensing images

： Military aircraft recognition in remote sensing images locates military aircraft in remote sensing images and classify them at a fine-grained level. It plays a vital role in reconnaissance and early warning, intelligence analysis, and other fields. However, the development of military aircraft recognition in remote sensing images is relatively slow due to the lack of publicly available datasets. Therefore, constructing a high-quality and large-scale military aircraft recognition dataset is important. This study constructs a public remote sensing image military aircraft recognition dataset called MAR20 to promote the research progress in this field. The dataset has the following characteristics: (1) MAR20 is currently the largest remote sensing image military aircraft recognition dataset, which includes 3842 images, 20 types, and 22341 instances. Each instance has a horizontal bounding box and also an oriented bounding box. (2) Given that all fine-grained types belong to the aircraft category, different types of aircraft often have similar characteristics, which result in high similarity of different types of targets. (3) Large intra-class differences exist between targets of the same type due to the influence of climate, season, illumination, occlusion, and even the atmospheric scattering in the process of remote sensing imaging. To establish a benchmark for military aircraft recognition in remote sensing images, this paper study evaluates seven commonly used horizontal object recognition methods, namely, Faster R-CNN, RetinaNet, ATSS, FCOS, Cascade R-CNN, TSD, and Double-Head, as well as eight oriented object recognition methods, namely, Faster R-CNN-O, RetinaNet-O, RoI Transformer, Gliding Vertex, Double-Head-O, Oriented R-CNN, FCOS-O, and S2A-Net, on the MAR20 dataset. Through experimental comparisons in the tasks of horizontal object recognition and oriented object recognition, two-stage methods are proven to be more effective in target recognition than one-stage methods. In this study