Research on Automatic Microalgae Detection System Based on Deep Learning

doi:10.16476/j.pibb.2022.0629

	Research on Automatic Microalgae Detection System Based on Deep Learning
	Xiang, Rui-Jie 1; Liu, Hao 1; Lu, Zhen 2; Xiao, Ze-Yu 1; Liu, Hai-Peng 1; Wang, Yin-Chu 2,3; Peng, Xiao 1; Yan, Wei 1
刊名	PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS
	2024
卷号	51 期号:1 页码:177-189
关键词	microalgae detection brightfield microscopy deep learning object detection
ISSN号	1000-3282
DOI	10.16476/j.pibb.2022.0629
通讯作者	Peng, Xiao(pengxiao_px@szu.edu.cn) ; Yan, Wei(weiyan@szu.edu.cn)
英文摘要	Objective The scale of microalgae farming industry is huge. During farming, it is easy for microalgae to be affected by miscellaneous bacteria and other contaminants. Because of that, periodic test is necessary to ensure the growth of microalgae. Present microscopy imaging and spectral analysis methods have higher requirements for experiment personnel, equipment and sites, for which it is unable to achieve real-time portable detection. For the purpose of real-time portable microalgae detection, a real-time microalgae detection system of low detection requirement and fast detection speed is needed. Methods This study has developed a microalgae detection system based on deep learning. A microscopy imaging device based on bright field was constructed. With imaged captured from the device, a neural network based on YOLOv3 was trained and deployed on microcomputer, thus realizing real-time portable microalgae detection. This study has also improved the feature extraction network by introducing cross-region residual connection and attention mechanism and replacing optimizer with Adam optimizer using multistage and multimethod strategy. Results With cross-region residual connection, the mAP value reached 0.92. Compared with manual result, the detection error was 2.47%. Conclusion The system could achieve real-time portable microalgae detection and provide relatively accurate detection result, so it can be applied to periodic test in microalgae farming.
WOS关键词	RESEARCH PROGRESS ; IDENTIFICATION ; CHINA ; ALGAE
WOS研究方向	Biochemistry & Molecular Biology ; Biophysics
语种	英语
WOS记录号	WOS:001157349700011
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China ; Key Project of Department of Education of Guangdong Province ; Guangdong Basic and Applied Basic Research Foundation ; Shenzhen Science and Technology Program
内容类型	期刊论文
源URL	[http://ir.yic.ac.cn/handle/133337/34947]
专题	中国科学院烟台海岸带研究所
通讯作者	Peng, Xiao; Yan, Wei
作者单位	1.Shenzhen Univ, Minist Educ & Guangdong Prov, Shenzhen Key Lab Photon & Biophoton, Key Lab Optoelect Devices & Syst,Coll Phys & Optoe, Shenzhen 518060, Peoples R China 2.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China 3.Natl Bas Sci Data Ctr, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Xiang, Rui-Jie,Liu, Hao,Lu, Zhen,et al. Research on Automatic Microalgae Detection System Based on Deep Learning[J]. PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS,2024,51(1):177-189.
APA	Xiang, Rui-Jie.,Liu, Hao.,Lu, Zhen.,Xiao, Ze-Yu.,Liu, Hai-Peng.,...&Yan, Wei.(2024).Research on Automatic Microalgae Detection System Based on Deep Learning.PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS,51(1),177-189.
MLA	Xiang, Rui-Jie,et al."Research on Automatic Microalgae Detection System Based on Deep Learning".PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS 51.1(2024):177-189.