[1]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]
WANG Changan,TIAN Jinwen.Fine-grained inshore ship recognition assisted by deep-learning generative adversarial networks[J].CAAI Transactions on Intelligent Systems,2020,15(2):296-301.[doi:10.11992/tis.201901004]
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WANG Changan,TIAN Jinwen.Fine-grained inshore ship recognition assisted by deep-learning generative adversarial networks[J].CAAI Transactions on Intelligent Systems,2020,15(2):296-301.[doi:10.11992/tis.201901004]
生成对抗网络辅助学习的舰船目标精细识别
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第2期
页码:
296-301
栏目:
学术论文—机器学习
出版日期:
2020-03-05
- Title:
- Fine-grained inshore ship recognition assisted by deep-learning generative adversarial networks
- Keywords:
- remote sensing image; inshore ships; ships detection; ships classification; fine-grained ships classification; generative adversarial networks; deep-learning; image processing
- 分类号:
- TP391
- 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。
- 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.
- 参考文献/References:
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备注/Memo
收稿日期:2019-01-06。
基金项目:国家自然科学基金项目(61273279)
作者简介:王昌安，硕士研究生，主要研究方向为遥感图像处理、计算机视觉;田金文，教授，博士生导师，中国电子学会高级会员。主要研究方向为计算机视觉及其应用、机器学习及其应用、自动目标识别及应用、遥感图像信息处理、目标光学特性建模与成像仿真。主持国家自然科学基金项目1项，国家863项目多项，发表学术论文20余篇
通讯作者:田金文.E-mail:jwtian@mail.hust.edu.cn
更新日期/Last Update:
1900-01-01