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

Jiaxing Mao; Hao Dou; Jinwen Tian

doi:10.1117/12.2537974

14 February 2020 G-CNN type recognition of typical aircraft based on target characteristics

Jiaxing Mao, Hao Dou, Jinwen Tian

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114300C (2020) https://doi.org/10.1117/12.2537974
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

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.

Citation Download Citation

Jiaxing Mao, Hao Dou, and Jinwen Tian "G-CNN type recognition of typical aircraft based on target characteristics", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300C (14 February 2020); https://doi.org/10.1117/12.2537974

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KEYWORDS

3D modeling

Data modeling

Detection and tracking algorithms

3D acquisition

Target recognition

Convolution

3D image processing

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