정보처리학회논문지:소프트웨어 및 데이터공학 (KIPS Transactions on Software and Data Engineering)

  • 제10권8호
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  • Pages.311-318
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  • 2021
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  • 2287-5905(pISSN)
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  • 2734-0503(eISSN)
한국정보처리학회 (Korea Information Processing Society)
권호 표지
DOI QR코드

DOI QR Code

인공 위성 사진 내 선박 탐지 정확도 향상을 위한 Watershed 알고리즘 기반 RoI 축소 기법

Watershed Algorithm-Based RoI Reduction Techniques for Improving Ship Detection Accuracy in Satellite Imagery

  • 이승재 (고려대학교 정보보호대학원) ;
  • 윤지원 (고려대학교 정보보호대학원)
  • 투고 : 2021.01.11
  • 심사 : 2021.05.17
  • 발행 : 2021.08.31
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⟨ 이전 논문 다음 논문 ⟩

초록

해상 안보, 국제 동향 파악 등 다양한 이유로 해상 사진에서 선박을 탐지하고자하는 연구는 지속되어 왔다. 인공지능의 발달로 인해 사진 및 영상 내 객체 탐지를 위한 R-CNN 모델이 등장하였고 객체탐지의 성능이 비약적으로 상승하였다. R-CNN 모델을 이용한 해상 사진에서의 선박 탐지는 인공위성 사진에도 적용되기 시작하였다. 하지만 인공위성 사진은 넓은 지역을 투사하기 때문에 선박 외에도 차량, 지형, 건물 등 다양한 객체들이 선박으로 인식되는 경우가 있다. 본 논문에서는 R-CNN계열 모델을 이용한 인공위성 사진에서의 선박 탐지의 성능을 개선하기 위한 새로운 방법론을 제안한다. 표지자 기반 watershed 알고리즘을 통해 육지와 바다를 분리하고 morphology 연산을 수행하여 RoI를 한 차례 더 특정한 뒤 특정된 RoI에 R-CNN 계열 모델을 사용하여 선박을 탐지하여 오탐을 줄인다. 해당 방법을 이용하여 Faster R-CNN을 사용하였을 경우, Faster R-CNN만을 사용했을 때에 비해 오탐률을 80% 줄일 수 있었다.

Research has been ongoing to detect ships from offshore photographs for a variety of reasons, including maritime security, identifying international trends, and social scientific research. Due to the development of artificial intelligence, R-CNN models for object detection in photographs and images have emerged, and the performance of object detection has risen dramatically. Ship detection in offshore photographs using the R-CNN model has also begun to apply to satellite photography. However, satellite images project large areas, so various objects such as vehicles, landforms, and buildings are sometimes recognized as ships. In this paper, we propose a novel methodology to improve the performance of ship detection in satellite photographs using R-CNN series models. We separate land and sea via marker-based watershed algorithm and perform morphology operations to specify RoI one more time, then detect vessels using R-CNN family models on specific RoI to reduce typology. Using this method, we could reduce the misdetection rate by 80% compared to using only the Fast R-CNN.

키워드

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