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Conference Paper A Comparative Study on the Maritime Object Detection Performance of Deep Learning Models
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Authors
SungWon Moon, Jiwon Lee, Jungsoo Lee, Dowon Nam, Wonyoung Yoo
Issue Date
2020-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1155-1157
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289620
Abstract
With the increasing volume of maritime traffic, the need for maritime surveillance is also increasing. In this situation, unlike the land environment where various data sets are built, the marine environment lacks data, so the progress of technology research is insufficient. Several object detection methods have been proposed and show excellent performance in areas where sufficient data is secured, but performance verification of existing techniques has not been performed on marine images. In this paper, we compare the performance of marine object detection in various marine images with the latest object detection methods and propose an object detection method suitable for marine environments.
KSP Keywords
Data sets, Detection Method, Marine environment, Maritime Traffic, Maritime surveillance, Performance verification, comparative study, deep learning(DL), deep learning models, detection performance, excellent performance