Algae detection equipment and detection method

Abstract

The invention relates to the technical field of algae detection, and discloses an algae detection device and method, and the device comprises an embedded platform, the embedded platform is provided with a model operation environment, and is transplanted with a trained Yolo v4 network-based algae identification model; a training process of the algae identification model: acquiring a microscopic image of an algae sample, expanding sample data based on a GAN network, and marking the type and position of algae in the microscopic image; constructing an algae identification model based on a Yolo v4 network, and performing model training on the algae identification model by using the marked sample data; and during algae sample detection, importing a microscopic image of an algae sample to be detected, detecting through the trained algae identification model, and outputting a detection result. According to the method, the algae condition in the water body can be quickly and accurately monitored in real time under the condition that the marked sample is insufficient, the deployment is convenient, the prediction result is objective and direct, and the labor cost is greatly reduced.

Description

technical field [0001] The invention relates to the technical field of algae detection, in particular to an algae detection device and a detection method. Background technique [0002] Studying the distribution of algae in waters has a directional effect on the study of water pollution. There are many kinds of algae with different shapes. In addition, some algae are also toxic, especially the toxic algae living in some sewage will have a great impact on the treatment of organic domestic sewage. At present, the traditional methods for classification and identification of algae in real water samples mainly analyze the magnitude of algae from the side by analyzing the content of chlorophyll in the water body through manual microscope inspection and statistics. Manual inspection is time-consuming and laborious, the process is cumbersome, and the inspection results are easily affected by subjective judgments. At the same time, visual fatigue caused by long-term work can also ea...

AI Technical Summary

Problems solved by technology

Manual inspection is time-consuming and laborious, the process is cumbersome, and the inspection results are easily affected by subjective judgments. At the same time, visual fatigue caused by long-term work can also easily reduce the accuracy of inspection

The chlorophyll inference method cannot accurately obtain the distribution of algae, and the cost of obtaining the content of various algae is high

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