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Conference Paper A Comparative Study on the Maritime Object Detection Performance of Deep Learning Models
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Authors
Moon Sung-Won, Jiwon Lee, Lee Jung Soo, Do-Won Nam, Yoo Wonyoung
Issue Date
202010
Source
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1155-1157
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289620
Project Code
20HH5100, Development of precise content identification technology based on relationship analysis for maritime vessel/structure , Do-Won Nam
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, Object detection, Performance verification, comparative study, deep learning(DL), deep learning models, detection performance