GS2-1 Microalgae Detection by Digital Image Processing and Artificial Intelligence

Title:	GS2-1 Microalgae Detection by Digital Image Processing and Artificial Intelligence
Publication:	ICAROB2023
Volume:	28
Pages:	852-857
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2023.GS2-1
Author(s):	Watcharin Tangsuksant, Pornthep Sarakon
Publication Date:	February 9, 2023
Keywords:	Microalgae detection, Melosira, Oscillatoria, YOLO
Abstract:	This article presents a technical approach to the video computer analysis, to automatically identifying the two most frequently identified microalgae in water supplies. To handle some difficulties encountered in image segmentation problem such as unclear algae boundary and noisy background, we proposed a deep learning-based method for classifiers or localizers to perform microalgae detection and counting process. The system achieves approximately 91% accuracy on Melosira and Oscillatoria detection, which around 4.82 seconds per grid. (Intel Xeon(R) CPU E52667 12 CPU at 2.66GHz and 32.0GB RAM, NVIDIA Quadro K5200 with 2304 CUDA cores). The system can significantly reduce 33.33 - 55.56% of the counting time when compared with the visual inspection of manual methods, and eliminate the error due to the human fatigue.
PDF File:	https://alife-robotics.co.jp/members2023/icarob/data/html/data/GS/GS2/GS2-1.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/

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