[1]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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Fine-grained inshore ship recognition assisted by deep-learning generative adversarial networks

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 2 Page number: 296-301 Column: 学术论文—机器学习 Public date: 2020-03-05
Title:
Fine-grained inshore ship recognition assisted by deep-learning generative adversarial networks
Author(s):
WANG Chang’an;  TIAN Jinwen
College of Automation, National Key Laboratory of Multispectral Information Processing Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Keywords:
remote sensing image; inshore ships; ships detection; ships classification; fine-grained ships classification; generative adversarial networks; deep-learning; image processing
CLC:
TP391
DOI:
10.11992/tis.201901004
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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