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

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Study on Detection Technique for Coastal Debris by using Unmanned Aerial Vehicle Remote Sensing and Object Detection Algorithm based on Deep Learning

무인항공기 영상 및 딥러닝 기반 객체인식 알고리즘을 활용한 해안표착 폐기물 탐지 기법 연구

  • 박수호 (부경대학교 지구환경시스템과학부) ;
  • 김나경 (부경대학교 지구환경시스템과학부) ;
  • 정민지 (부경대학교 지구환경시스템과학부) ;
  • 황도현 (부경대학교 지구환경시스템과학부) ;
  • 엥흐자리갈 운자야 (부경대학교 지구환경시스템과학부) ;
  • 김보람 (부경대학교 지구환경시스템과학부) ;
  • 박미소 (부경대학교 지구환경시스템과학부) ;
  • 윤홍주 (부경대학교 지구환경시스템과학부) ;
  • 서원찬 (부경대학교 신소재시스템공학과)
  • Received : 2020.10.30
  • Accepted : 2020.12.15
  • Published : 2020.12.31
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Abstract

In this study, we propose a method for detecting coastal surface wastes using an UAV(Unmanned Aerial Vehicle) remote sensing method and an object detection algorithm based on deep learning. An object detection algorithm based on deep neural networks was proposed to detect coastal debris in aerial images. A deep neural network model was trained with image datasets of three classes: PET, Styrofoam, and plastics. And the detection accuracy of each class was compared with Darknet-53. Through this, it was possible to monitor the wastes landing on the shore by type through unmanned aerial vehicles. In the future, if the method proposed in this study is applied, a complete enumeration of the whole beach will be possible. It is believed that it can contribute to increase the efficiency of the marine environment monitoring field.

본 연구에서는 무인항공기 원격탐사 기법과 딥러닝 기반 객체인식 알고리즘을 활용한 해안표착폐기물 탐지기법을 제안한다. 항공영상 내에 존재하는 해안표착폐기물을 탐지하기 위해 심층신경망 기반 객체 인식 알고리즘을 제안하였다. PET, 스티로폼, 기타 플라스틱의 3가지 클래스의 이미지 데이터셋으로 심층신경망 모델을 훈련시켰으며, 각 클래스별 탐지 정확도를 Darknet-53과 비교하였다. 이를 통해 해안표착 폐기물을 무인항공기를 통해 성상별 모니터링할 수 있었으며, 향후 본 연구에서 제안하는 방법이 적용될 경우 해변 전체에 대한 성상별 전수조사가 가능하며, 이를 통해 해양환경 감시 분야의 효율성 증대에 기여할 수 있을 것으로 판단된다.

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References

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