November 2022

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https://digitallibrary.mes.ac.in/handle/1/2490

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Browsing November 2022 by Author "Fa-ming Shao"
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    Visual-simulation-region-proposal-and-generative-adversarial-_2022_Defence-T
    (ScienceDirect, 2022-11-05) Fan-jie Meng ; Yong-qiang Li ; Fa-ming Shao ; Gai-hong Yuan ; Ju-ying Dai
    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.