1. Visual-simulation region proposal and generative adversarial network based ground military target recognition.
- Author
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Fan-jie Meng, Yong-qiang Li, Fa-ming Shao, Gai-hong Yuan, and Ju-ying Dai
- Subjects
MILITARY applications of virtual reality ,HIGH resolution imaging ,TARGET acquisition ,BIG data ,BATTLEFIELDS - 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 noisyrepresentation. In this paper, a recognition method, involving a novel visual attention mechanismbased Gabor region proposal sub-network (Gabor RPN) and improved refinement generative adversarial 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. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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