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Blast furnace sintered ore particle size detection method and system based on RGB and laser feature fusion

A technology of feature fusion and detection method, applied in neural learning method, character and pattern recognition, image data processing and other directions, can solve the problem of low detection accuracy of blast furnace sinter particle size, and achieve the effect of improving segmentation accuracy and detection accuracy

Pending Publication Date: 2021-12-31
CENT SOUTH UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0014] The blast furnace sinter particle size detection method and system based on the fusion of RGB and laser features provided by the present invention solves the technical problem of low detection accuracy of the existing blast furnace sinter particle size

Method used

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  • Blast furnace sintered ore particle size detection method and system based on RGB and laser feature fusion
  • Blast furnace sintered ore particle size detection method and system based on RGB and laser feature fusion
  • Blast furnace sintered ore particle size detection method and system based on RGB and laser feature fusion

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Embodiment 1

[0072] The blast furnace sinter particle size detection method based on fusion of RGB and laser features provided by Embodiment 1 of the present invention includes:

[0073] Step S101, collecting RGB images of blast furnace sintered ore on the blast furnace charge conveyor belt and single-line laser detection data to obtain RGB data sets and laser data sets;

[0074] Step S102, constructing a deep learning network with an encoder-decoder structure, the deep learning network includes an RGB encoding branch, a laser encoding branch, a fusion branch and a decoding process;

[0075]Step S103, according to the RGB coding branch and the laser coding branch, respectively perform feature extraction on the RGB data set and the laser data set, and obtain RGB initial feature tensors and laser initial feature tensors;

[0076] Step S104, constructing a multi-source feature weighted fusion sub-network in the last layer of the fusion branch, and based on the multi-source feature weighted fu...

Embodiment 2

[0080] The blast furnace sinter particle size detection method based on RGB and laser feature fusion in the embodiment of the present invention includes:

[0081] Step S201, collecting RGB images of blast furnace sintered ore on the blast furnace charge conveyor belt and single-line laser detection data to obtain RGB data sets and laser data sets.

[0082] Specifically, in order to obtain high RGB images and Laser detection data, RGB image information and Laser depth information are collected through hardware devices such as industrial cameras and laser scanners. Among them, the laser detection equipment is the LMS4111R-13000 laser line scanner produced by SICK, and the data acquisition software is SOPAS Engineering Tool. The industrial camera is the German Basler acA2500-14gm Basler industrial area array camera data acquisition software is pylon Viewer. Both use Ethernet to communicate. Acquisition system such as figure 1 as shown, figure 1 In the embodiment of the presen...

Embodiment 3

[0134] In this embodiment, the 2650m in a certain ironworks 3 The specific steps of the blast furnace ore image segmentation method based on the weighted fusion of RGB and laser features are as follows:

[0135] 1. Data set production. In order to obtain RGB images of blast furnace sinter and Laser detection data, the present invention collects RGB image information and Laser depth information through hardware devices such as industrial cameras and laser scanners on the blast furnace feeding belt of a steel plant. Among them, the laser detection equipment is the LMS4111R-13000 laser line scanner produced by SICK, and the data acquisition software is SOPAS Engineering Tool. The industrial camera is the German Basler acA2500-14gmBasler industrial area array camera data acquisition software is pylon Viewer. Both use Ethernet to communicate. A total of 8 sets of data are collected in a concentrated period of time by industrial cameras and laser scanners (each time a set of data...

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Abstract

The invention discloses a blast furnace sinter particle size detection method and system based on RGB and laser feature fusion, and the method comprises the steps: constructing a deep learning network of an encoder-decoder structure through obtaining an RGB data set and a laser data set, obtaining an RGB initial feature tensor and a laser initial feature tensor, constructing a multi-source feature weighted fusion sub-network at the last layer of the fusion branch, carrying out adaptive weighted fusion on the RGB initial feature tensor and the laser initial feature tensor based on the multi-source feature weighted fusion sub-network to obtain a fusion feature tensor, and inputting the fusion feature tensor into a decoding process to obtain a segmented image. The technical problem that the existing blast furnace sinter particle size detection precision is low is solved, the multi-source feature weighted fusion sub-network is added in the deep learning network of the encoder-decoder structure, the adaptive weighted fusion of the multi-source features can be realized, and the detection precision of the blast furnace sinter particle size is improved. The laser supplement effect is fully exerted, and the detection precision of the blast furnace sintered ore particle size is improved.

Description

technical field [0001] The invention mainly relates to the technical field of blast furnace ironmaking, in particular to a blast furnace sinter particle size detection method and system based on fusion of RGB and laser features. Background technique [0002] The blast furnace is the key equipment for ironmaking in the iron and steel industry, and its stability determines the quality of ironmaking. The blast furnace production process mainly includes four systems: air supply system, charging system, slagging system and heat system. The four basic systems are interdependent and affect each other, which directly affect the work and forward status of the blast furnace and then affect the quality of ironmaking. The optimization of the charging system can make the gas distribution in the furnace reasonable, improve the contact conditions between the ore and the gas, reduce the resistance of the gas to the furnace charge, and avoid the blast furnace from holding the wind and hangin...

Claims

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Application Information

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IPC IPC(8): G06T7/62G06T7/00G06T7/10G06K9/62G06N3/04G06N3/08
CPCG06T7/62G06T7/0004G06T7/10G06N3/084G06T2207/10024G06T2207/10028G06N3/045G06F18/253
Inventor 蒋朝辉刘金狮何瑞清余金花桂卫华张海峰
Owner CENT SOUTH UNIV
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