The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
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Application of the artificial intelligence for automatic detection of shipping noise in shallow-water

천해역 선박 소음 자동 탐지를 위한 인공지능 기법 적용

  • 김선효 (한국해양과학기술원 해양방위안전연구센터) ;
  • 정섬규 (한국해양과학기술원 해양방위안전연구센터) ;
  • 강돈혁 (한국해양과학기술원 해양방위안전연구센터) ;
  • 김미라 (한국해양과학기술원 해양방위안전연구센터) ;
  • 조성호 (한국해양과학기술원 해양방위안전연구센터)
  • Received : 2020.06.16
  • Accepted : 2020.06.29
  • Published : 2020.07.31
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Abstract

The study on the temporal and spatial monitoring of passing vessels is important in terms of protection and management the marine ecosystem in the coastal area. In this paper, we propose the automatic detection technique of passing vessel by utilizing an artificial intelligence technology and broadband striation patterns which are characteristic of broadband noise radiated by passing vessel. Acoustic measurements to collect underwater noise spectrum images and ship navigation information were conducted in the southern region of Jeju Island in South Korea for 12 days (2016.07.15-07.26). And the convolution neural network model is optimized through learning and validation processes based on the collected images. The automatic detection performance of passing vessel is evaluated by precision (0.936), recall (0.830), average precision (0.824), and accuracy (0.949). In conclusion, the possibility of the automatic detection technique of passing vessel is confirmed by using an artificial intelligence technology, and a future study is proposed from the results of this study.

항행 선박의 시·공간적 모니터링 기술 연구는 연안 해양공간에서 해양 생태계 보호 및 효율적인 관리를 위해서 중요하다. 본 연구에서는 실험해역에서 측정된 선박 소음 특징인 광대역 줄무늬 패턴 자료에 인공지능 기술을 적용하여 항행하는 선박을 자동 탐지하는 연구를 수행하였다. 소음 스펙트럼 이미지와 선박의 항행정보를 수집하기 위한 해상시험은 2016년 7월 15일부터 26일까지 제주 남부 해역에서 실시되었고, 컨볼루션 신경망 모델은 수집된 이미지를 기반으로 학습, 교차검증 과정을 거쳐 최적화되었다. 선박 소음 자동 탐지 기법의 성능은 정밀도(0.936), 재현율(0.830), 평균 정밀도(0.824) 그리고 정확도(0.949)로 평가되었다. 결론적으로 인공지능 기법을 활용하여 선박 소음의 자동 탐지 가능성을 확인하였다. 본 연구의 결과로부터 성능을 향상시킬 수 있는 방안 및 향후 연구에 대하여 제안하였다.

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References

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