Deep learning-based fancy carp screening method and device

A screening method and deep learning technology, applied in the fields of instrument, calculation, character and pattern recognition, etc., can solve the problem of difficult screening of koi seedlings, and achieve the effect of reducing labor costs

Active Publication Date: 2017-07-25
NANJING UNIV OF SCI & TECH
View PDF5 Cites 19 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The technical problem solved by the present invention is to provide a koi screening method and system device based on deep learning to solve the problem that koi seedli

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Deep learning-based fancy carp screening method and device
  • Deep learning-based fancy carp screening method and device
  • Deep learning-based fancy carp screening method and device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0020] combine figure 1 , figure 2 , a kind of koi screening method based on "deep learning" of the present invention comprises the following steps:

[0021] S1: Collect a preset amount of koi pictures and perform a normalized preprocessing operation of centralization and size standardization on each picture to obtain the feature vector (training sample) of the image;

[0022] Among them, the preset amount can be based on actual needs, through the image acquisition unit (camera) to collect koi body color and back piebald characteristic images, and perform normalization processing preprocessing operations of centralization and size standardization, and each image is processed The size is 32×32 (that is, 1024 pixels), and the dimension of the image feature vector is guaranteed to be the same as the number of random units in the input layer (set the number of units to 1024); the number of output units in the output layer can be the same as the category of the data sample to be ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The present invention discloses a deep learning-based fancy carp screening method and a device. The method comprises the steps of collecting fancy carp images of a preset number, and subjecting each image to the centralized, size-standardized and normalized pre-treatment so as to obtain the feature vectors of the image; subjecting a deep belief network model to unsupervised pre-training and supervised fine tuning by utilizing a training sample, and outputting the extracted feature vectors of the training sample after being trained; adopting the output of the trained deep belief network as the input of a support vector machine classifier, and training the classifier so as to obtain corresponding parameters; and classifying the fancy carp by utilizing the trained classifier. According to the technical scheme of the invention, the deep belief network model for training a large amount of fancy carp image data is adopted and applied to the screening process for the high-quality seedlings of the fancy carp. Therefore, the dependence of the labor operation and breeding enterprises on the professional technical personnel is greatly reduced. The screening precision and the screening efficiency are improved.

Description

technical field [0001] The invention belongs to the technical field of computational pattern recognition, in particular to a koi screening method and device based on deep learning. Background technique [0002] Deep learning is a new research field in machine learning theory and an extension of the field of artificial intelligence. Its motivation lies in establishing and simulating the mechanism of the human brain to interpret data and analyze and learn artificial neural networks. The advantage of deep learning is to learn more useful features by building a machine learning model with multiple hidden layers and massive training data, so as to improve the accuracy of classification or prediction. It has a wide range of applications in image classification, voice recognition and text information screening in the Internet field, but it has not been involved in fishery, especially the feature recognition of koi seedlings. [0003] At present, existing domestic koi breeding ente...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/62
CPCG06F18/217G06F18/2411G06F18/214
Inventor 杨晨石必坤王嵩杰
Owner NANJING UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap