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Fruit defect classification method based on compressed sensing

A classification method and compression sensing technology, applied in the direction of optical testing flaws/defects, etc., can solve the problems of limited promotion and application, complicated processing process, long execution time, etc., to promote economic development, fast classification speed, and high accuracy. Effect

Inactive Publication Date: 2014-06-25
SHAANXI UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The processing process of the method is complicated, the amount of information is large, and the execution time is long, which limits its practical promotion and application in the field of agricultural production to a certain extent.

Method used

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  • Fruit defect classification method based on compressed sensing

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Effect test

Embodiment 1

[0022] The present invention is a method for grading apple defects based on compressed sensing, using first-class fruit (apples without surface defects) as the measured object, including the following steps:

[0023] Step 1, obtain the main view and two left and right side views of the tested apple through the CCD camera;

[0024] Step 2, respectively extracting the R component images corresponding to the RGB images of the left and right side views, and adopting a color image space mean filter to smooth and filter the R component images of the left and right views respectively, so as to reduce the noise of the image;

[0025] Step 3: Transform the left and right side views from the RGB model space to the HIS model space, extract the corresponding H component images, and use the color image space mean filter to smooth and filter the H component images of the left and right views respectively to reduce the image distortion. noise;

[0026] Step 4: Take the 3*3 area in the upper...

Embodiment 2

[0034] Second-class fruit (with surface defects and the total area is not greater than 1cm 2 Apple) as the measured object, including the following steps:

[0035] Step 1, obtain the main view and two left and right side views of the tested apple through the CCD camera;

[0036] Step 2, respectively extracting the R component images corresponding to the RGB images of the left and right side views, and adopting a color image space mean filter to smooth and filter the R component images of the left and right views respectively, so as to reduce the noise of the image;

[0037] Step 3: Transform the left and right side views from the RGB model space to the HIS model space, extract the corresponding H component images, and use the color image space mean filter to smooth and filter the H component images of the left and right views respectively to reduce the image distortion. noise;

[0038] Step 4: Take the 3*3 area in the upper left corner of the R component image after the filt...

Embodiment 3

[0046] With the third-class fruit (the total area of ​​surface defects is greater than 1cm 2 Apple) as the measured object, including the following steps:

[0047] Step 1, obtain the main view and two left and right side views of the tested apple through the CCD camera;

[0048] Step 2, respectively extracting the R component images corresponding to the RGB images of the left and right side views, and adopting a color image space mean filter to smooth and filter the R component images of the left and right views respectively, so as to reduce the noise of the image;

[0049] Step 3: Transform the left and right side views from the RGB model space to the HIS model space, extract the corresponding H component images, and use the color image space mean filter to smooth and filter the H component images of the left and right views respectively to reduce the image distortion. noise;

[0050] Step 4: Take the 3*3 area in the upper left corner of the R component image after the filt...

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Abstract

A fruit defect classification method based on compressed sensing. The method comprises the following steps: extracting R component images corresponding to a left side view and a right side view of a fruit to be measured, and carrying out smoothing filtering to reduce noise; conversing the left side view and the right side view from an RGB model space into an HIS model space, extracting corresponding H component images of the left side view and the right side view, and carrying out smoothing filtering to reduce noise; carrying out slide scanning on the R and H component images treated with filtering to realize coarse segmentation; respectively carrying out sparse decomposition on the processed R component image and H component image of the left side view and the right side view to determine a dividing point of important feature information and secondary information, and assigning a weighted value on the important feature information; respectively adding the sparse results of the R component and the H component corresponding to the left side view and the right side view to obtain new coefficient vectors; multiplying a signal encoding measurement matrix and the new coefficient vectors, and carrying out encoding measurement to obtain values characterizing fruit defect; and observing distribution regularity of the above values through a large number of sample trainings, so as to obtain a threshold measuring the grade of fruit defect, and outputting a classification result of fruit defect.

Description

technical field [0001] The invention relates to a method for realizing automatic non-destructive detection of agricultural product quality by using digital image processing technology, in particular to a fruit defect classification method based on compressed sensing. Background technique [0002] China is a large fruit producing country, and fast and accurate detection and grading of fruits is an important measure to improve the economic benefits of fruits and enhance the international competitiveness of the industry. [0003] The traditional manual grading method relies on the experience and visual inspection of skilled workers to judge the quality of fruits, which is difficult to guarantee the accuracy and effectiveness of the results and cannot meet the requirements of the market. The existing computer vision-based fruit grading method uses conventional digital image processing algorithms to calculate characteristic parameters such as fruit area and defect area through pr...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N21/88
Inventor 党宏社张芳杨小青田丽娜姚勇张新院郭楚佳
Owner SHAANXI UNIV OF SCI & TECH