| 生成对抗网络辅助学习的舰船目标精细识别 |
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| 引用本文： | 王昌安,田金文. 生成对抗网络辅助学习的舰船目标精细识别[J]. 智能系统学报, 2020, 15(2): 296-301. DOI: 10.11992/tis.201901004 |
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| 作者姓名： | 王昌安 田金文 |
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| 作者单位： | 华中科技大学 多谱信息处理国家级重点实验室, 武汉 湖北 430074 |
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| 摘 要： | 针对近岸舰船目标细粒度识别的难题，提出了一种利用生成对抗网络辅助学习的任意方向细粒度舰船目标识别框架。通过训练能模仿舰船目标区域的抽象深度特征的生成网络引入生成样本，来辅助分类子网络学习样本空间的流形分布，从而增强细粒度的类别间判别能力。在细粒度类别的近岸舰船数据集上，引入生成对抗网络后的算法识别准确率得到较大提升，平均识别精度提升了2%。消融实验结果表明，利用生成样本辅助训练分类子网络可以有效地提升舰船目标的细粒度识别精度。
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| 关 键 词： | 遥感图像 近岸舰船 舰船目标检测 舰船识别 舰船细粒度分类 生成对抗网络 深度学习 图像处理 |
Fine-grained inshore ship recognition assisted by deep-learning generative adversarial networks |
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| WANG Chang’an,TIAN Jinwen. Fine-grained inshore ship recognition assisted by deep-learning generative adversarial networks[J]. CAAL Transactions on Intelligent Systems, 2020, 15(2): 296-301. DOI: 10.11992/tis.201901004 |
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| Authors: | WANG Chang’an TIAN Jinwen |
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| Affiliation: | College of Automation, National Key Laboratory of Multispectral Information Processing Technology, Huazhong University of Science and Technology, Wuhan 430074, China |
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| Abstract: | To solve the fine-grained inshore ship recognition problem, a multidirectional fine-grained ship recognition framework, which is based on deep-learning generative adversarial networks, is proposed. By training the generation network that can simulate the abstract depth features of the ship target area, the generated samples are used to assist the classification subnetwork in learning the manifold distribution of the sample space. Thus, the fine-grained discriminating power of the classification subnetwork is enhanced. Ablation experiment was conducted on the multi-category fine-grained inshore ship dataset, and the model assisted by generative adversarial networks achieved an average precision rate improvement of 2%. As shown in the comparative experiment, it is beneficial to train the classification subnetwork using the generated samples to solve the fine-grained inshore ship recognition problem. |
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| Keywords: | remote sensing image inshore ships ships detection ships classification fine-grained ships classification generative adversarial networks deep-learning image processing |
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