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In recent years, with the continuous development of research related to environmental microorganisms and molecular biotechnology, the application of environmental microorganisms to solve water quality and environmental problems has been widely used. In this paper, based on the deep learning neural network ECA-RetinaNet model, using the seventh edition of the Environmental Microbial Database (EMDS-7) in 42 categories of 2365 images of environmental microorganisms for recognition and classification processing, in accordance with the ratio of 6:2:2 divided into the training set, the validation set and the test set, and validate the effectiveness of the model, to realize the environmental microorganisms micrographic image recognition and classification technology. At the same time, combining unmanned vehicle, unmanned aircraft, robotic arm, automatic cruise program, computer vision, a three-dimensional integrated environmental health microbial detection system of water, land and air for daily autonomous water quality testing and environmental microbial identification and classification, with a wide range of use scenarios, high detection efficiency, and convenience, was designed. It aims to improve the accuracy of environmental microorganisms microscopic image recognition and classification, and at the same time, reduce the work pressure and risk of environmental related staff, improve the detection efficiency, and promote the development of the field of environmental health microorganisms.

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Computer Vision-Based Remote Monitoring Kit for Environmental Microorganisms

  • 会议论文

  • 发表日期：

    2025年

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  • Xinyu Ouyang,
  • Yuhang Yang,
  • Han Yu,
  • Xuhan Zhang,
  • Qixuan Niu,
  • Marcin Grzegorzek,
  • Ning Xu,
  • Xin Zhao,
  • Chen Li

摘要

In recent years, with the continuous development of research related to environmental microorganisms and molecular biotechnology, the application of environmental microorganisms to solve water quality and environmental problems has been widely used. In this paper, based on the deep learning neural network ECA-RetinaNet model, using the seventh edition of the Environmental Microbial Database (EMDS-7) in 42 categories of 2365 images of environmental microorganisms for recognition and classification processing, in accordance with the ratio of 6:2:2 divided into the training set, the validation set and the test set, and validate the effectiveness of the model, to realize the environmental microorganisms micrographic image recognition and classification technology. At the same time, combining unmanned vehicle, unmanned aircraft, robotic arm, automatic cruise program, computer vision, a three-dimensional integrated environmental health microbial detection system of water, land and air for daily autonomous water quality testing and environmental microbial identification and classification, with a wide range of use scenarios, high detection efficiency, and convenience, was designed. It aims to improve the accuracy of environmental microorganisms microscopic image recognition and classification, and at the same time, reduce the work pressure and risk of environmental related staff, improve the detection efficiency, and promote the development of the field of environmental health microorganisms.