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Visual-simulation region proposal and generative adversarial network based ground military target recognition
Defence Technology ( IF 5.1 ) Pub Date : 2021-07-09 , DOI: 10.1016/j.dt.2021.07.001
Fan-jie Meng 1 , Yong-qiang Li 1 , Fa-ming Shao 2 , Gai-hong Yuan 1 , Ju-ying Dai 2
Affiliation  

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.



中文翻译:

基于视觉模拟区域建议和生成对抗网络的地面军事目标识别

地面军事目标识别在无人装备和掌握军事应用的战场动态方面发挥着至关重要的作用,但受到低分辨率和噪声表示的干扰。在本文中,提出了一种识别方法,涉及一种新的基于视觉注意机制的 Gabor 区域提议子网络 (Gabor RPN) 和改进的细化生成对抗子网络 (GAN)。提出了新的中心-外围竞争 3D 彩色 Gabor 滤波器来模拟视网膜结构,并在低层作为特征提取卷积核,以提高 Gabor RPN 的识别精度和框架训练效率。改进的细化GAN用于解决目标分类模糊的问题,包括一个生成器直接从小的模糊图像中生成大的高分辨率图像,以及一个鉴别器,不仅可以区分真实图像与假图像,还可以区分目标类别。构建了一个特殊的地面军事目标识别数据集,称为地面军事目标数据集(GMTD)。在 GMTD 数据集上进行的实验有效地证明了我们的方法在涉及低分辨率和噪声表示的目标时能够获得更好的节能和识别效果,从而保证了该算法具有良好的工程应用前景。

更新日期:2021-07-09
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