한국전자통신학회논문지 (The Journal of the Korea institute of electronic communication sciences)

한국전자통신학회 (Korea Institute of Electronic Communication Science)
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CCTV 영상과 합성곱 신경망을 활용한 해무 탐지 기법 연구

Study on Detection Technique for Sea Fog by using CCTV Images and Convolutional Neural Network

  • 김나경 (부경대학교 지구환경시스템과학부) ;
  • 박수호 (부경대학교 지구환경시스템과학부) ;
  • 정민지 (부경대학교 지구환경시스템과학부) ;
  • 황도현 (부경대학교 지구환경시스템과학부) ;
  • 앵흐자리갈 운자야 (부경대학교 지구환경시스템과학부) ;
  • 박미소 (부경대학교 지구환경시스템과학부) ;
  • 김보람 (부경대학교 지구환경시스템과학부) ;
  • 윤홍주 (부경대학교 공간정보시스템공학과)
  • 투고 : 2020.10.26
  • 심사 : 2020.12.15
  • 발행 : 2020.12.31
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초록

본 논문에서는 합성곱 신경망을 기반으로 CCTV 이미지를 통한 해무 탐지 방법을 제안한다. 학습에 필요한 자료로 시정 1km 기준으로 총 11개의 항만 또는 해수욕장(부산항, 부산신항, 평택항, 인천항, 군산항, 대산항, 목포항, 여수광양항, 울산항, 포항항, 해운대해수욕장)에서 수집된 해무와 해무가 아닌 이미지 10004장을 랜덤 추출하였다. 전체 10004장의 데이터셋 중에 80%를 추출하여 합성곱 신경망 모델 학습에 사용하였다. 사용된 모델은 16개의 합성곱층과 3개의 완전 연결층을 가지고 있으며, 마지막 완전 연결층에서 Softmax 분류를 수행하는 합성곱 신경망을 활용하였다. 나머지 20%를 이용하여 모델 정확도 평가를 수행하였고 정확도 평가 결과 약 96%의 분류 정확도를 보였다.

In this paper, the method of detecting sea fog through CCTV image is proposed based on convolutional neural networks. The study data randomly extracted 1,0004 images, sea-fog and not sea-fog, from a total of 11 ports or beaches (Busan Port, Busan New Port, Pyeongtaek Port, Incheon Port, Gunsan Port, Daesan Port, Mokpo Port, Yeosu Gwangyang Port, Ulsan Port, Pohang Port, and Haeundae Beach) based on 1km of visibility. 80% of the total 1,0004 datasets were extracted and used for learning the convolutional neural network model. The model has 16 convolutional layers and 3 fully connected layers, and a convolutional neural network that performs Softmax classification in the last fully connected layer is used. Model accuracy evaluation was performed using the remaining 20%, and the accuracy evaluation result showed a classification accuracy of about 96%.

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참고문헌

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