Visual-simulation region proposal and generative adversarial network based ground military target recognition
Ground military target recognition plays a crucial role in unmanned equipment and grasping the battlefield dynamics for military applications, but is disturbed by low-resolution and noisy-representation. In this paper, a recognition method, involving a novel visual attention mechanism-based Gabor re...
Saved in:
Published in | Defence technology Vol. 18; no. 11; pp. 2083 - 2096 |
---|---|
Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.11.2022
KeAi Communications Co., Ltd |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Ground military target recognition plays a crucial role in unmanned equipment and grasping the battlefield dynamics for military applications, but is disturbed by low-resolution and noisy-representation. In this paper, a recognition method, involving a novel visual attention mechanism-based Gabor region proposal sub-network (Gabor RPN) and improved refinement generative adversarial sub-network (GAN), is proposed. Novel central–peripheral rivalry 3D color Gabor filters are proposed to simulate retinal structures and taken as feature extraction convolutional kernels in low-level layer to improve the recognition accuracy and framework training efficiency in Gabor RPN. Improved refinement GAN is used to solve the problem of blurry target classification, involving a generator to directly generate large high-resolution images from small blurry ones and a discriminator to distinguish not only real images vs. fake images but also the class of targets. A special recognition dataset for ground military target, named Ground Military Target Dataset (GMTD), is constructed. Experiments performed on the GMTD dataset effectively demonstrate that our method can achieve better energy-saving and recognition results when low-resolution and noisy-representation targets are involved, thus ensuring this algorithm a good engineering application prospect. |
---|---|
ISSN: | 2214-9147 2214-9147 |
DOI: | 10.1016/j.dt.2021.07.001 |