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구분 SCI
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학술대회 A Study on the Improvement of Fine-grained Ship Classification through Data Augmentation Using Generative Adversarial Networks
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문성원, 이지원, 이정수, 오아름, 남도원, 유원영
International Conference on Information and Communication Technology Convergence (ICTC) 2021, pp.1230-1232
21HH5800, 영상 내 객체간 관계 분석 기반 해상 선박/구조물 상세 식별 콘텐츠 기술 개발, 남도원
Identification of the type of ship is an important issue in maritime surveillance. However, unlike the land environment where data sets are easy to build, it is difficult to build large amounts of marine environment data where it is difficult to collect images. In this situation, the use of large-scale data obtained in the surveillance and defense fields is essential for research, but the use of data for private research is impossible due to security issues. In this paper, a ship dataset free from security problems was constructed through data augmentation using GANs. We conduct an experiment on improving fine-grained ship classification performance through the use of a small amount of real ship images and augmented data, and try to show that the augmented data is useful for ship classification network training.
KSP 제안 키워드
Classification Performance, Data Augmentation, Data sets, Environment data, Large-scale data, Marine environment, Maritime surveillance, Network training, Security issues, Security problems, Ship classification