Visual-simulation region proposal and generative adversarial network based ground military target recognition |
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Institution: | 1. Department of Space Test and Launch, Noncommissioned Officer School, Space Engineering University, Beijing, 102299, China;2. Department of Mechanical Engineering, College of Field Engineering, Army Engineering University of PLA, Nanjing, 210007, China |
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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 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. |
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Keywords: | Deep learning Biological vision Military application Region proposal network Gabor filter Generative adversarial network |
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