Given the growing demand for military fine-grained object reconnaissance, warning, and intelligence analysis, the automated recognition of military objects using high-resolution remote sensing imagery has become increasingly significant. Nevertheless, the existing remote sensing military object recognition data sets are characterized by a limited number of fine-grained category levels and the difficulty in encompassing the typical military objects. This limits the accurate recognition of military objects in different scenes and lacks the association between different class levels. To address the above issues, this research uses satellite remote sensing data platforms, such as Google Earth and PIE Engine, to collect high-resolution remote sensing image data from developed countries. Military objects are identified across various scenes, including air, sea, and ground. Combined with common related standards, this research uses rotating boxes and a four-level category system (scene, kind, function, and type) to conduct annotations. A military fine-grained object recognition data set named MFOR (for Military Fine-grained Object Recognition; data set download link: https: //drive. google. com/file/d/1sjH7p94wM16fPrₛhCjZ4o0swDUSVP2N/view? usp=driveₗink) has been constructed and made publicly accessible. MFOR is a large-scale military object fine-grained recognition data set that covers different scenes, including sea, land, and air. It consists of 12 633 images, 145 distinct fine-grained categories, and 38 759 object instances. To establish a benchmark for military fine-grained object recognition in remote sensing imagery, this research conducts experiments and analysis on eight rotating frame object recognition networks using the MFOR data set, aiming to offer valuable references for related research.
Yan et al. (Thu,) studied this question.