Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Automatic recognition and classification of ore and mineral images

A technology of automatic identification and classification methods, applied in character and pattern recognition, biological neural network models, instruments, etc., can solve the problem of less overall application, achieve the effects of eliminating subjectivity, improving work efficiency, and good robustness

Inactive Publication Date: 2019-01-29
SUN YAT SEN UNIV
View PDF4 Cites 26 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in geological work, big data and artificial intelligence research is still in its infancy, and the overall application is still relatively small
How to build an automatic identification and classification system for rocks and minerals under the microscope based on the concept and technological progress of the current era of big data and artificial intelligence is still a scientific and technological problem that has not yet been well resolved

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
  • Automatic recognition and classification of ore and mineral images
  • Automatic recognition and classification of ore and mineral images

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0023] The identification and classification method of the present invention comprises the following steps:

[0024] Step 1: Collect microscopic photos covering various types of rocks and minerals, and uniformly adjust image parameters and image segmentation and labeling;

[0025] Step 2: change the pixel position of the input photo to obtain more input image data, and realize image data increase processing;

[0026] Step 3: According to the image characteristics of rock minerals, use the Unet segmentation network model to learn the whole rock photos under the microscope according to the mineral types through the supervised training of the machine model. The specific explanation of the framework level is as follows:

[0027] Step1: In the input layer, directly input the original rock mineral image data into the network for training, and convert the original rock mineral image under the microscope into a feature image of 572*572 size;

[0028] Step2: The input layer propagate...

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

An automatic recognition and classification of ore and mineral images are disclosed in the invention. the invention utilizes the computer vision technology and the depth convolution neural network theory, Based on the big data platform Tensorflow, the convolution artificial neural network model is established, and the image data input model is trained according to the microscopic photographs of yellow iron ore from Jiapigou Gold Mine in Jilin Province, so as to realize the automatic recognition and classification of different ore minerals in the microscopic photographs of yellow iron ore. Theinvention can assist geologists to identify and classify the microscopic photographs of ore minerals and improve the working efficiency of geologists.

Description

technical field [0001] The invention specifically relates to a method for automatic recognition and classification of ore mineral images under a microscope based on deep learning. Background technique [0002] The identification and classification of rock minerals under the microscope is an indispensable link in geological work, and also plays a fundamental and guiding role in the entire geological work. Existing microscopic identification and classification methods of rock minerals use artificial means to make one-sided judgments on the structure, content and other lithofacies characteristics of rock minerals from several limited views in a thin section. However, in geological work, the research on big data and artificial intelligence is still in its infancy, and the overall application is still relatively small. How to build an automatic identification and classification system for rocks and minerals under a microscope based on the concept and technological progress of th...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/34G06N3/04
CPCG06V10/267G06N3/045G06F18/214G06F18/241
Inventor 周永章徐述腾沈文杰张彦龙
Owner SUN YAT SEN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com