The Journal of the Convergence on Culture Technology (문화기술의 융합)

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
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A Study on the Implementation of Real-Time Marine Deposited Waste Detection AI System and Performance Improvement Method by Data Screening and Class Segmentation

데이터 선별 및 클래스 세분화를 적용한 실시간 해양 침적 쓰레기 감지 AI 시스템 구현과 성능 개선 방법 연구

  • 왕태수 (동의대학교 컴퓨터 공학과) ;
  • 오세영 (동의대학교 IT융합학과) ;
  • 이현서 (동의대학교 산업ICT기술공학과) ;
  • 최동규 (동의대학교 스마트IT연구소) ;
  • 장종욱 (동의대학교 컴퓨터 공학과) ;
  • 김민영 (동의대학교 ICT융복합연구소)
  • Received : 2022.04.28
  • Accepted : 2022.05.08
  • Published : 2022.05.31
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Abstract

Marine deposited waste is a major cause of problems such as a lot of damage and an increase in the estimated amount of garbage due to abandoned fishing grounds caused by ghost fishing. In this paper, we implement a real-time marine deposited waste detection artificial intelligence system to understand the actual conditions of waste fishing gear usage, distribution, loss, and recovery, and study methods for performance improvement. The system was implemented using the yolov5 model, which is an excellent performance model for real-time object detection, and the 'data screening process' and 'class segmentation' method of learning data were applied as performance improvement methods. In conclusion, the object detection results of datasets that do screen unnecessary data or do not subdivide similar items according to characteristics and uses are better than the object recognition results of unscreened datasets and datasets in which classes are subdivided.

해양침적쓰레기는 유령어업으로 인한 폐어구들로 인해 많은 피해와 쓰레기 추정량 편차 증가 등의 문제를 일으키는 주요 원인이 된다. 본 논문에서는 폐어구 사용량, 유통량, 유실량, 회수량에 대한 실태 파악을 위해 실시간 해양침적쓰레기 감지 인공지능 시스템을 구현하고, 성능 개선을 위한 방법에 대해 연구한다. 실시간 객체인식에 우수한 성능모델인 yolov5모델을 활용하여 시스템을 구현하였고, 성능개선 방법으로는 학습데이터의 '데이터 선별 과정'과 '클래스 세분화' 방법을 적용하였다. 결론적으로 비선별된 데이터셋과 클래스가 세분화된 데이터셋의 객체인식 결과보다 불필요한 데이터를 선별하거나 특징 및 용도에 따라 유사 항목을 세분화 하지 않은 데이터셋의 객체인식 결과는 해양침적쓰레기 인식에 개선된 결과를 보인다.

Keywords

Acknowledgement

본 논문(저서)는 부산광역시 및 (재)부산인재평생교육진흥원의 BB21플러스 사업으로 지원된 연구임. 또한 본 연구는 과학기술정보통신부 및 정보통신기획평가원의 지역지능화 혁신인재양성(Grand ICT연구센터) 사업의 연구결과로 수행되었음.(IITP-2022-2016-0-00318)

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