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Conference Paper A Comparative Study on the Ship Classification Performance of the Deep Learning Model According to Dataset Difference
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Authors
SungWon Moon, YoonHyung Kim, Dowon Nam, Wonyoung Yoo, Changick Kim
Issue Date
2019-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.1428-1430
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC46691.2019.8940015
Abstract
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.
KSP Keywords
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