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학술대회 A Comparative Study on the Ship Classification Performance of the Deep Learning Model According to Dataset Difference
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저자
문성원, 김윤형, 남도원, 유원영, 김창익
발행일
201910
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.1428-1430
DOI
https://dx.doi.org/10.1109/ICTC46691.2019.8940015
협약과제
19HS6700, 영상 내 객체간 관계 분석 기반 해상 선박/구조물 상세 식별 콘텐츠 기술 개발, 남도원
초록
Along with the development of deep learning, object detection and classification performance in the field of computer vision has made a breakthrough. In order to classify objects with high accuracy, it is important to select an optimal deep learning model for each environment. Especially for non-typical environments, there is a high probability that the deep learning model will perform differently than expected. In this paper, we compare and evaluate the ship classification performance of the existing deep learning model for the marine environment where it is difficult to acquire the data set, and experimentally change the performance according to the dataset change.
키워드
CNN, Maritime Image, Ship Classification
KSP 제안 키워드
Classification Performance, Computer Vision(CV), Data sets, High accuracy, Learning model, Marine environment, Object detection, Ship classification, comparative study, deep learning(DL), detection and classification