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G-CNN type recognition of typical aircraft based on target characteristics

This paper is aimed at the type recognition of aircraft, with four kinds of typical military aircraft as research objects. In this paper, we establish a database on aircraft type and propose an effective and efficient method of type recognition called Geometric-Convolutional Neutral Networks(G-CNN) in a coarse-to-fine manner. We start with target characteristics for the first time and establish a target characteristics database by analyzing the acquired characteristics such as geometric characteristics and optical characteristics. Next, aiming at the problem that the dataset on aircraft types is few, we build 3D models based on the characteristics database and make an aircraft type dataset using 3D simulation creatively, which is of great significance for the research on aircraft type recognition. Finally, we extract the geometric characteristics of the aircraft—affine invariant moments and aspect ratios, realizing a fast and efficient region selecting; we improve residual blocks with dilated convolution, which is used for type recognition for the first time. Our method achieves 89.0%mAP and the experiments show that it tackles the type recognition problems with improved performance.