[논문]딥러닝 사물 인식 알고리즘(YOLOv3)을 이용한 미세조류 인식 연구

박정수; 백지원; 유광태; 남승원; 김종락

doi:10.15681/kswe.2021.37.4.275

딥러닝 사물 인식 알고리즘(YOLOv3)을 이용한 미세조류 인식 연구
Microalgae Detection Using a Deep Learning Object Detection Algorithm, YOLOv3 원문보기

한국물환경학회지 = Journal of Korean Society on Water Environment, v.37 no.4, 2021년, pp.275 - 285

박정수 (국립한밭대학교 건설환경공학과) , 백지원 ((주)유앤유) , 유광태 ((주)유앤유) , 남승원 (국립낙동강생물자원관 원생생물연구팀) , 김종락 ((주)유앤유)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (12)

그림 Fig. 1. Example of algae cell image labelling.
표 Table 1. Algae images used for YOLOv3 model development
그림 Fig. 2. Schematic of area of overlap and area of union for IOU calculation.
그림 Fig. 3. Schematic of YOLOv3 structure.
그림 Fig. 4. Attributes of a bounding box. n is the number of classes for prediction.
그림 Fig. 5. mAP and PR of YOLOv3 model for test data set.
그림 Fig. 6. Detection confidence of Desmodesmus communis for five, 10, 20 and 30 genera data sets.
그림 Fig. 7. Detection of Selenastrum capricornutum for five, 10, 20 and 30 genera models.
그림 Fig. 8. Detection of Selenastrum capricornutum and Pectinodesmus pectinatus for 20 genera models.
그림 Fig. 9. Detection of Acutodesmus obliquus for five, 10, 20 and 30 genera models.
표 Table 2. Accuracy of five, 10, 20 and 30 genera model for misclassification case
그림 Fig. 10. Detection confidence of Micrasterias sp. with color and grayscale images.

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