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Real-time monitoring system of cyanobacteria blooms using deep learning approach

  • 1220: Visual and Sensory Data Processing for Real Time Intelligent Surveillance System
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Abstract

In recent years, a large number of cyanobacteria formed harmful blooms. The traditional method of preventing and controlling cyanobacteria blooms is to manually observe the video image to find the outbreak site of cyanobacteria, and manually prompt the salvage or chemical treatment according to the degree of cyanobacteria outbreak. Due to the traditional methods are difficult to early warning timely and accurately, we designed a real-time monitoring system of cyanobacteria blooms. The system can automatically identify cyanobacteria in the video image and calculate the coverage rate of cyanobacteria to realize the automatic warning of cyanobacteria bloom outbreak. First, we propose EGAN (Enhanced Generative Adversarial Networks) algorithm to improve the accuracy of cyanobacteria identification, the precision is 94.03%, the recall is93.65%. Second, we use the coverage rate of cyanobacteria to determine if (and when) early warning of cyanobacteria salvage. The proposed cyanobacterial video monitoring system not only helps Environmental Monitoring Center of Wuxi to provide early warning of the outbreak of cyanobacteria in real time, but also provides effective strategies for subsequent prevention and control by analyzing the cyanobacterial video data.

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Acknowledgements

This work is supported Jiangsu Key Laboratory of Media Design and Software Technology. We also thank Environmental Monitoring Center of Wuxi for providing us with video data of cyanobacteria.

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Authors and Affiliations

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, 214122, China

    LiFang Chen & Yu Shi

  2. Jiangsu Key Laboratory of Media Design and Software Technology, Wuxi, Jiangsu, 214122, China

    LiFang Chen

  3. Environmental Monitoring Center of Wuxi, Wuxi, Jiangsu, 214121, China

    YuanXin Du

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  1. LiFang Chen
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  2. Yu Shi
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  3. YuanXin Du
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Correspondence to LiFang Chen.

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Chen, L., Shi, Y. & Du, Y. Real-time monitoring system of cyanobacteria blooms using deep learning approach. Multimed Tools Appl 81, 42413–42431 (2022). https://doi.org/10.1007/s11042-022-13490-y

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  • DOI: https://doi.org/10.1007/s11042-022-13490-y

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