Research on Automatic Microalgae Detection System Based on Deep Learning
10.16476/j.pibb.2022.0629
- VernacularTitle:基于深度学习的微藻自动检测系统研究
- Author:
Rui-Jie XIANG
1
;
Hao LIU
1
;
Zhen LU
2
;
Ze-Yu XIAO
1
;
Hai-Peng LIU
1
;
Yin-Chu WANG
2
;
Xiao PENG
1
;
Wei YAN
1
Author Information
1. College of Physics and Optoelectronic Engineering, Shenzhen Key Laboratory of Photonics and Biophotonics, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen 518060, China
2. Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
- Publication Type:Journal Article
- Keywords:
microalgae detection;
brightfield microscopy;
deep learning;
object detection
- From:
Progress in Biochemistry and Biophysics
2024;51(1):177-189
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveThe 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. MethodsThis 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. ResultsWith cross-region residual connection, the mAP value reached 0.92. Compared with manual result, the detection error was 2.47%. ConclusionThe 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.
