한국물환경학회지 (Journal of Korean Society on Water Environment)

  • 제37권4호
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  • Pages.275-285
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  • 2021
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  • 2289-0971(pISSN)
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  • 2289-098X(eISSN)
한국물환경학회 (Korean Society on Water Environment)
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딥러닝 사물 인식 알고리즘(YOLOv3)을 이용한 미세조류 인식 연구

Microalgae Detection Using a Deep Learning Object Detection Algorithm, YOLOv3

  • 투고 : 2021.05.11
  • 심사 : 2021.07.14
  • 발행 : 2021.07.30
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초록

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.

키워드

과제정보

This work was supported by Korea Environment Industry &Technology Institute (KEITI) through Aquatic Ecosystem Conservation Research Program, funded by Korea Ministry of Environment (MOE) (2020003030006) and the Nakdonggang National Institute of Biological Resources (NNIBR), funded by the Ministry of Environment (MOE) of the Republic of Korea (NNIBR202101103).

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