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Automatic marine plankton classification method based on convolutional neural network and digital holography

A convolutional neural network and plankton technology, which is applied in the field of automatic image classification, can solve the problems of difficulty in obtaining accurate reconstruction distance of holographic images, manpower and time consumption, and a large number of iterative operations, so as to reduce the amount of data and training time, improve the Efficiency, the effect of saving computing resources

Pending Publication Date: 2020-09-29
ZHEJIANG UNIV OF TECH
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Problems solved by technology

The reconstruction of holographic images is difficult to obtain an accurate reconstruction distance, and the automatic reconstruction process requires a large number of iterative operations, so there are high requirements for hardware, and it also consumes manpower and time.

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  • Automatic marine plankton classification method based on convolutional neural network and digital holography
  • Automatic marine plankton classification method based on convolutional neural network and digital holography
  • Automatic marine plankton classification method based on convolutional neural network and digital holography

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Embodiment Construction

[0031] The present invention will be further described below in conjunction with accompanying drawing:

[0032] refer to Figure 1 ~ Figure 3 , an automatic classification method for marine plankton based on convolutional neural network and digital holography, including the following steps:

[0033] 1) see figure 2 The process of taking plankton images is to place the marine plankton specimen on the stage, and use the CCD camera to capture the holographic image of the sample, including the following steps:

[0034] 1.1) Design the shooting optical path, select the appropriate laser light source and CCD camera, and place the laser light source, industrial camera, stage, and samples to be tested according to the designed optical path;

[0035] 1.2) Use a CCD camera to take a holographic image of the specimen, and adjust the distance between the specimen and the photosensitive element of the CCD camera to obtain holographic images with different focal lengths. The holographic ...

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Abstract

The invention discloses an automatic marine plankton classification method based on a convolutional neural network and digital holography. The method comprises the following steps: 1) shooting a holographic image of the marine plankton by using a digital holographic system; and 2) constructing a convolutional neural network model, setting a convolutional layer number, a convolutional kernel size,training parameters and a loss function, inputting the pictures obtained in the step 1) into a neural network, and operating the neural network to obtain a final classification result. The invention discloses a marine plankton rapid classification method based on a digital holographic image by combining a digital holographic technology with a deep learning technology in order to meet the requirements of high efficiency, low cost and rapidity aiming at wide application of a current digital holographic system to marine plankton.

Description

technical field [0001] The invention relates to an image automatic classification method, in particular to a method for automatic classification of marine plankton based on convolutional neural network and digital holography technology. Background technique [0002] Marine plankton are an important part of marine ecosystems. The study of marine plankton is of great significance to the monitoring and protection of marine ecological environment. There are many kinds of marine plankton, which are rich in variety and widely distributed. Therefore, the observation, classification and statistics of marine plankton have important research value. (Digital holography is one of them.) Researchers have proposed many methods to study marine plankton, including optical plankton recorder (OPR), optical plankton counter (OPC) and digital holography (DH). DH is a powerful technology that enables three-dimensional (3-D) recording, high-resolution imaging, and non-contact measurement. The...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G06T7/90
CPCG06N3/08G06T7/90G06T2207/10052G06T2207/30181G06N3/045G06F18/214G06F18/24
Inventor 张怡龙卢耀翔王海霞陈朋梁荣华
Owner ZHEJIANG UNIV OF TECH
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