Journal of the Korea Institute of Military Science and Technology (한국군사과학기술학회지)

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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A Study on the Improvement of Submarine Detection Based on Mast Images Using An Ensemble Model of Convolutional Neural Networks

컨볼루션 신경망의 앙상블 모델을 활용한 마스트 영상 기반 잠수함 탐지율 향상에 관한 연구

  • Jeong, Miae (Department of Defense Science, National Defense University) ;
  • Ma, Jungmok (Department of Defense Science, National Defense University)
  • 정미애 (국방대학교 국방과학학과) ;
  • 마정목 (국방대학교 국방과학학과)
  • Received : 2020.02.17
  • Accepted : 2020.03.30
  • Published : 2020.04.05
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Abstract

Due to the increasing threats of submarines from North Korea and other countries, ROK Navy should improve the detection capability of submarines. There are two ways to detect submarines : acoustic detection and non-acoustic detection. Since the acoustic-detection way has limitations in spite of its usefulness, it should have the complementary way. The non-acoustic detection is the way to detect submarines which are operating mast sets such as periscopes and snorkels by non-acoustic sensors. So, this paper proposes a new submarine non-acoustic detection model using an ensemble of Convolutional Neural Network models in order to automate the non-acoustic detection. The proposed model is trained to classify targets as 4 classes which are submarines, flag buoys, lighted buoys, small boats. Based on the numerical study with 10,287 images, we confirm the proposed model can achieve 91.5 % test accuracy for the non-acoustic detection of submarines.

Keywords

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