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> 한국물환경학회 > 한국물환경학회지 > 37권 4호

딥러닝 사물 인식 알고리즘(YOLOv3)을 이용한 미세조류 인식 연구

Microalgae Detection Using a Deep Learning Object Detection Algorithm, YOLOv3

박정수 ( Jungsu Park ) , 백지원 ( Jiwon Baek ) , 유광태 ( Kwangtae You ) , 남승원 ( Seung Won Nam ) , 김종락 ( Jongrack Kim )

- 발행기관 : 한국물환경학회

- 발행년도 : 2021

- 간행물 : 한국물환경학회지, 37권 4호

- 페이지 : pp.275-285 ( 총 11 페이지 )


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구매 전에 간행물명, 페이지 수 확인 부탁 드립니다.

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논문제목
초록(외국어)
Algal bloom is an important issue in maintaining the safety of the drinking water supply system. Fast detection and classification of algae images are essential for the management of algal blooms. Conventional visual identification using a microscope is a labor-intensive and time-consuming method that often requires several hours to several days in order to obtain analysis results from field water samples. In recent decades, various deep learning algorithms have been developed and widely used in object detection studies. YOLO is a state-of-the-art deep learning algorithm. In this study the third version of the YOLO algorithm, namely, YOLOv3, was used to develop an algae image detection model. YOLOv3 is one of the most representative one-stage object detection algorithms with faster inference time, which is an important benefit of YOLO. A total of 1,114 algae images for 30 genera collected by microscope were used to develop the YOLOv3 algae image detection model. The algae images were divided into four groups with five, 10, 20, and 30 genera for training and testing the model. The mean average precision (mAP) was 81, 70, 52, and 41 for data sets with five, 10, 20, and 30 genera, respectively. The precision was higher than 0.8 for all four image groups. These results show the practical applicability of the deep learning algorithm, YOLOv3, for algae image detection.

논문정보
  • - 주제 : 공학분야 > 환경공학
  • - 발행기관 : 한국물환경학회
  • - 간행물 : 한국물환경학회지, 37권 4호
  • - 발행년도 : 2021
  • - 페이지 : pp.275-285 ( 총 11 페이지 )
  • - UCI(KEPA) :
저널정보
  • - 주제 : 공학분야 > 환경공학
  • - 성격 : 학술지
  • - 간기 : 격월
  • - 국내 등재 : KCI 등재
  • - 해외 등재 : -
  • - ISSN : 2289-0971
  • - 수록범위 : 1985–2021
  • - 수록 논문수 : 2360