Conveyor belt ore rock particle image segmentation method

An image segmentation and conveyor belt technology, applied in the field of image processing, can solve the problems of high image definition and noise requirements, complex parameter adjustment, low segmentation accuracy, etc., to solve noise and edge discontinuity, high segmentation accuracy, avoid Adjust the effect of interference

Active Publication Date: 2019-06-07
NORTHEASTERN UNIV
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Problems solved by technology

[0005] The present invention aims at the existing image segmentation method which has low segmentation precision when processing the ore image of the conveyor belt, has high requirements for image clarity and noise, and has complicated parameter adjustment and the problem of poor adaptability, in order to solve the above-mentioned problems of the prior art, the present invention provides a method for image segmentation of ore and rock particles in conveyor belts

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  • Conveyor belt ore rock particle image segmentation method
  • Conveyor belt ore rock particle image segmentation method
  • Conveyor belt ore rock particle image segmentation method

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Embodiment 1

[0044] Such as figure 1 As shown, the present invention provides a method for image segmentation of ore and rock particles in a conveyor belt, which specifically includes the following steps:

[0045] In this embodiment, the U-Net neural network is used to construct the first convolutional neural network, and the Res_Unet neural network is used to construct the second convolutional neural network, wherein the U-Net network is a fully convolutional network obtained based on FCN improvement, The Res_UNet network is based on the semantic segmentation model of ResNet (Residual Neural Network) and U-Net. Unlike the U-Net network, the Res_UNet network adds the residual module to the U-Net network, which is easier to train and improves the training of the model. The speed also allows the network to obtain relatively few parameters without losing accuracy, such as figure 2 as shown,

[0046] Obtaining the first convolutional neural network model trained in advance comprises the fol...

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Abstract

The invention relates to a conveyor belt ore rock particle image segmentation method. The method comprises the following steps: 101, preprocessing a to-be-segmented conveyor belt ore rock particle image, and inputting the to-be-segmented conveyor belt ore rock particle image into a pre-trained first convolutional neural network model to obtain a first contour map; 102, inputting the first contourmap into a pre-trained second convolutional neural network model to obtain a second contour map; 103, performing binarization processing on the second contour map by using a preset threshold, and performing morphological closed operation processing on the binarized map to obtain a third contour map; 104, obtaining the contour, the minimum bounding rectangle of the contour and the area of the contour in the third contour map, screening by using the minimum bounding rectangle of the contour and the area of the contour, and drawing the screened contour as a segmentation map. According to the method, deep learning is utilized to realize conveyor belt ore rock image contour detection and ore rock size distribution automatic statistics, the requirement for image definition is reduced, image segmentation is accurate, and application is convenient.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for segmenting images of ore and rock particles on a conveyor belt. Background technique [0002] The ore particle size information is an important indicator to reflect the working status of the crusher in the mineral processing process. The detection effect of the ore particle size on the conveyor belt directly determines the production efficiency of the entire mineral processing process; A great breakthrough has been made. Among them, watershed method and its improved method, threshold segmentation method and segmentation method based on specific theory are the main ones; [0003] The watershed algorithm has a good response to weak edges, but for the noise in the image and the subtle gray level changes on the surface of the object, it will cause over-segmentation. When using the watershed algorithm to process mine rock images, it cannot deal with ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/12G06T7/13G06T7/155G06T7/136G06T7/11
Inventor 柳小波张育维赵胜
Owner NORTHEASTERN UNIV
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