Visual-simulation-region-proposal-and-generative-adversarial-_2022_Defence-T
Visual-simulation-region-proposal-and-generative-adversarial-_2022_Defence-T
Date
2022-11-05
Authors
Fan-jie Meng
Yong-qiang Li
Fa-ming Shao
Gai-hong Yuan
Ju-ying Dai
Journal Title
Journal ISSN
Volume Title
Publisher
ScienceDirect
Abstract
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 adversa-
rial sub-network (GAN), is proposed. Novel centraleperipheral 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.