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

Ore classification and size grading method and device based on deep learning network

A deep learning network, granularity classification technology, applied in sorting, image analysis, character and pattern recognition, etc., to achieve the effect of improving efficiency, improving accuracy, and reducing energy consumption for crushing

Pending Publication Date: 2022-04-22
ANSTEEL GRP MINING CO LTD
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to provide a method and device for ore classification and particle size classification based on deep learning network to solve the problem that the image recognition technology that analyzes ore category and particle size distribution needs to be improved in the prior art

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
  • Ore classification and size grading method and device based on deep learning network
  • Ore classification and size grading method and device based on deep learning network
  • Ore classification and size grading method and device based on deep learning network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 2

[0100] An ore classification and particle size classification device based on deep learning network, such as image 3 shown, including:

[0101] The image acquisition module is used to intercept the output ore video stream of the industrial camera to obtain the image to be predicted;

[0102] Image preprocessing module for preprocessing the labeled sample image data, including contrast-limited adaptive histogram equalization and white noise removal sub-modules;

[0103] The Mask-RCNN neural network model module is used to call the trained convolutional neural network model to predict the preprocessed ore image, and output the specific gravity of various ore types and ore particle size information;

[0104] The image storage module is used to store the images processed by the Mask-RCNN neural network model;

[0105] The human-computer interaction module is used to display real-time video and provide corresponding functions in the graphical user interface;

[0106] The statis...

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 invention provides an ore classification and particle size grading method and device based on a deep learning network in order to solve the problem that in the prior art, an image recognition technology for analyzing ore categories and particle size distribution at the same time needs to be improved, and belongs to the technical field of ore dressing. According to the method, the improved Faster-RCNN target detection network in computer vision is applied to extract features, classification and positioning of ores are completed at the same time, and an artificially designed feature extractor is replaced; through the ore position extracted by the target detection network, segmenting the ore by using an FCN semantic segmentation network to obtain granularity information of the ore; moreover, the high-quality image information is obtained by carrying out the data preprocessing work of contrast-limited adaptive histogram equalization, white noise removal and the like on the image; and meanwhile, the ore is classified and segmented by combining target detection and semantic segmentation technologies, so that the efficiency of the crusher is improved, the crushing energy consumption is reduced, and guidance is provided for subsequent processes.

Description

technical field [0001] The invention belongs to the technical field of mineral processing, and in particular relates to an ore classification, positioning and particle size classification detection method and device based on target detection and semantic segmentation Mask-RCNN deep learning network. Background technique [0002] The type and particle size distribution of the ore is an important basis for evaluating the crushing effect and subsequent processing of the ore. The categories are now mainly divided into magnetite, hematite, limonite and siderite. The particle size parameters mainly include area, perimeter , particle size and volume, etc. The traditional detection method is to use the inefficient, offline manual screening detection method to determine the particle size distribution of the ore after crushing, and experts to manually determine the general category of the ore, which is difficult to meet the needs of beneficiation production. [0003] In recent years,...

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): G06K9/62B07C5/342G06T7/10G06V10/764
CPCG06T7/10B07C5/3422B07C5/3425G06T2207/10004G06F18/241
Inventor 胡健杨晓峰陈子一苑庆波宋军肖成勇董振海王宇
Owner ANSTEEL GRP MINING CO LTD
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