CN105891230A - Fruit appearance detection method based on spectral image analysis - Google Patents
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- 235000013399 edible fruits Nutrition 0.000 title claims abstract description 45
- 230000003595 spectral effect Effects 0.000 title claims abstract description 38
- 238000001514 detection method Methods 0.000 title claims abstract description 36
- 238000010191 image analysis Methods 0.000 title claims abstract description 16
- 238000000034 method Methods 0.000 claims abstract description 11
- 238000001228 spectrum Methods 0.000 claims description 53
- 238000007689 inspection Methods 0.000 claims description 5
- 238000013507 mapping Methods 0.000 claims description 3
- 240000000902 Diospyros discolor Species 0.000 claims 1
- 230000006378 damage Effects 0.000 abstract description 19
- 238000004458 analytical method Methods 0.000 abstract description 10
- 238000000513 principal component analysis Methods 0.000 abstract description 4
- 238000003709 image segmentation Methods 0.000 abstract description 2
- 230000011218 segmentation Effects 0.000 abstract description 2
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- 244000287680 Garcinia dulcis Species 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 2
- 239000000470 constituent Substances 0.000 description 2
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- 238000002329 infrared spectrum Methods 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
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- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
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Abstract
The invention discloses a fruit appearance detection method based on spectral image analysis. The method comprises the steps that characteristic wavelengths adopted when spectral image analysis detection is conducted on fruit to be detected are predetermined; a spectral image collecting system detects the appearance surface of the fruit through the characteristic wavelengths, spectral component images corresponding to all the characteristic wavelengths are acquired separately, and principal component analysis is conducted on all the spectral component images to obtain an initial detection analysis chart; the initial detection analysis chart is preprocessed to obtain an image to be segmented, image segmentation is conducted on the image to be segmented by adopting a self-adaptive threshold value segmentation method, a slight damage area image of the fruit is completely segmented out from the image to be segmented, and then fruit appearance detection and recognition are achieved. According to the method, slight appearance damage of various fruits can be effectively recognized, and the detection and recognition accuracy rate is high.
Description
The application is to CN2014104528793 (applying date: 2014-09-05, patent name: based on light
The fruit appearance detection method of spectrum graphical analysis) divisional application made.
Technical field
The present invention relates to a kind of image recognition technology, especially relate to a kind of water based on spectral image analysis
Really appearance detection method.
Background technology
Visible images detection technique can solve the problem that the test problems of the notable surface defect of fruit, but to injury with blood-stasis
Relatively limited Deng the power of test then ratio of fruit surface slight damage.And near-infrared spectral analysis technology is in inside
Defect and slight damage context of detection have preferable sensitivity and an accuracy of detection, but near-infrared spectrum analysis
Belonging to a measurement, there is the strongest randomness in slight damage appearance position on Fructus Jujubae, so detection essence
Degree is difficult to ensure that.
Have paper at present or monograph disclose the hyper-spectral image technique detection technique to fruit outer surface,
Such as, agricultural mechanical journal, 2008,39 (1): 106-109, disclose and " utilize hyper-spectral image technique
Detection fruit slight damage " paper (author: Zhao Jiewen, Liu Jianhua, Chen Quansheng, etc.), author with
Fructus Mali pumilae is object of study, by the high spectrum image of 500nm~900nm is carried out PCA, chooses 547nm
Image under wavelength, extracts slight damage region by digital image processing method, slight damage detection
Accuracy is 88.57%.
Technical scheme disclosed in above paper, although demonstrate high light spectrum image-forming analytical technology and damage at fruit
The feasibility of context of detection, but still suffer from following technological deficiency: all EO-1 hyperion component images are led
Component analysis (Principal Components Analysis, PCA) converts, although dimensionality reduction speed,
But participate in conversion owing to crossing multi-wavelength, be easily caused much noise and introduce and reduce PCA transform effect, thus
Have impact on identification and accuracy of detection.
Summary of the invention
The present invention proposes a kind of fruit appearance detection method based on spectral image analysis, by extracting spectrum
Characteristic wavelength, and utilize characteristic wavelength main constituent image the slight damage of fruit surface is identified inspection
Survey.
The present invention adopts the following technical scheme that realization: a kind of fruit appearance based on spectral image analysis detects
Method, it includes step:
The graphics standard calculating each wave band of spectrum picture is poorThe graphics standard selected is poorMaximum a
Spectral band (is called for short " wave band " or " wave band vector " again);
Calculate any correlation coefficient between wave band vector o and wave band vector p in above-mentioned a spectral band
Rop, selects b minimum wave band vector of coefficient R op as preliminary characteristic wavelength;
Utilize Spectral angle mapping method, above-mentioned b wave band vector calculates treating of not damaged surface respectively
Spectrum between the spectrum picture c detecting fruit and the spectrum picture d of the fruit to be detected having injured surface
Angle β, is defined as fruit to be detected by the band combination that spectrum angle β is maximum and carries out spectral image analysis inspection
The characteristic wavelength used during survey;
Spectrum picture acquisition system utilizes characteristic wavelength to detect fruit outer surface, it is achieved fruit appearance is slightly damaged
Hinder the detection identification in region;
Wherein, a and b is integer and b < a.
Wherein, graphics standard is poorBe calculated as:
M and N represents line number and the columns of certain wave band respectively, and (i is j) that certain wave band is in spectrum picture to f
(m is certain wave band average gray value of all pixels in spectrum picture to pixel for i, gray value j).
Wherein, coefficient R op is calculated as:
moAnd mpIt is wave band o and the mean value vector of wave band p, x respectivelyoAnd xpRepresent wave band vector o respectively
With the gray value of pixel, 0≤Rop≤1 on wave band p.
Wherein, being calculated as of spectrum angle β:
xckAnd xdkIt is respectively two spectrum picture c and spectrum picture d spectral value on wave band k,
K=1,2 ..., b.
Compared with prior art, there is advantages that
The present invention uses the calculating of graphics standard difference, Calculation of correlation factor and spectral modeling calculating sifting for treating
Detection fruit carries out the characteristic wavelength used by spectrum picture identification, then by the spectral components of characteristic wavelength
Image is based on principal component analysis, it is achieved that the fusion of spectral image information, and by Image semantic classification and from
Adapt to Threshold segmentation and achieve the identification of fruit microscopic damage.It was verified that the present invention can effectively identify
Various fruit appearance slight damages, the correct recognition rata of identification test has reached more than 95%.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of one embodiment of the invention.
Detailed description of the invention
The present invention proposes a kind of fruit appearance detection method based on spectral image analysis, by extracting spectrum
Characteristic wavelength, and utilize characteristic wavelength main constituent image the slight damage of fruit surface is identified inspection
Surveying, detection identifies that fast and accurately rate is higher.
As it is shown in figure 1, in a preferred embodiment, the present invention includes step implemented as described below:
Step S1, the characteristic wavelength of predetermined spectrum picture, specifically come real by step S11~step S13
Existing.
Owing to spectrum picture wave band number is numerous, data volume is huge, directly carry out Object identifying and compare with classification
Difficulty, therefore before carrying out Object identifying, it is necessary to first pass through and spectrum picture is carried out Data Dimensionality Reduction extraction
Characteristic wavelength, i.e. select contain much information, dependency is little, classification separability is good band subset.
Step S11, carries out numerical Evaluation according to the quantity of information that each wave band of spectrum picture comprises, determines each ripple
Cross-talk collection comprises the size of quantity of information.
The present embodiment is poor by the graphics standard calculating the spectrum picture of each wave bandDetermine each wave band
Collection comprises the size of quantity of information.Graphics standard is poorReflect image intensity value and average gray value total from
Divergence, it is the important measure weighing amount of image information size, and its value is the biggest, and the quantity of information comprised is more
Greatly.Wherein, graphics standard is poorCalculating such as formula (1).
Wherein, M, N represent line number and the columns of certain wave band respectively, and (i is j) that certain wave band is at spectrogram to f
In Xiang, (m is certain wave band average gray of all pixels in spectrum picture to pixel for i, gray value j)
Value.
Step S12, the graphics standard calculating each wave band of spectrum picture is poorThe graphics standard selected is poor?
(wherein a can determine a the wave band of (or bigger) according to practical situation, and such as, a is spectrogram greatly
The 10-20% of the wave band sum of picture), calculate the correlation coefficient between this spectral band, to pick further
Except redundant variables.
Define the phase relation between any two wave band vector o and wave band vector p in this spectral band
Number Rop, calculates such as formula (2):
M in formulaoAnd mpIt is wave band o and the mean value vector of wave band p, x respectivelyoAnd xpRepresent wave band respectively
The gray value of pixel on vector o and wave band p, 0≤Rop≤1, Rop the biggest explanation the two wave band is vowed
Between amount o and wave band vector p, dependency is the strongest.
According to all coefficient R op calculated, select the b of coefficient R op minimum (or less)
(wherein b < a and b is integer) individual wave band vector is as preliminary characteristic wavelength.
Step S13, utilizes Spectral angle mapping method, calculates not damaged table respectively on this b wave band vector
Between the spectrum picture c and the spectrum picture d of fruit to be detected having injured surface of the fruit to be detected in face
Spectrum angle β, spectrum angle β the least then explanation the two spectrum picture the most similar.
The calculating such as formula (3) of spectrum angle β:
Spectrum angle calcu-lation such as formula (3), wherein: b is wave band number, can set b according to actual needs
Value, such as b=6;xckAnd xdkIt is respectively two spectrum picture c and spectrum picture d light on wave band k
Spectrum (k=1,2 ..., b).Such as, 10 wave bands selected are carried out the calculating of spectrum angle β, when
When wave band k spectrum angle β is the biggest, then the separability of two spectrum picture c and d of explanation is the best, therefore,
The wave band vectorial combination that spectrum angle β is maximum is determined when fruit to be detected carries out spectral image analysis detection
The characteristic wavelength used.
Therefore, by above-mentioned steps, step S1 can be for the predetermined inspection respectively of different detection fruit
The characteristic wavelength of spectrum picture during survey.
Step S2, spectrum picture acquisition system utilize the characteristic wavelength detection fruit appearance that step S1 determines
Face, it is thus achieved that Preliminary detection analysis chart.
When such as step S1 is for Fructus Mali pumilae outer surface damage check, it is previously determined 6 spies of spectrum picture
Levy wavelength, utilize these 6 characteristic wavelengths to detect Fructus Mali pumilae appearance respectively and obtain 6 corresponding spectral components figures
Then these 6 spectral components images are taked principal component analysis (Principal disclosed in prior art by picture
Components Analysis, PCA), obtain Preliminary detection analysis chart.
Step S3, that above-mentioned Preliminary detection analysis chart is carried out the image such as histogram equalization, medium filtering is pre-
After process, obtain image to be split, then use adaptive threshold fuzziness method to pretreated to be split
Image carries out image segmentation, i.e. can in image to be split by fruit slight damage area image by completely
Split, and there is not cut zone by mistake, it is achieved thereby that the Direct Recognition in slight damage region.
To sum up, the present invention uses the calculating of graphics standard difference, Calculation of correlation factor and spectral modeling calculating sifting
The characteristic wavelength used by spectrum picture identification is carried out, then by the light of characteristic wavelength for fruit to be detected
Spectral component image is based on principal component analysis, it is achieved that the fusion of spectral image information, and is located in advance by image
Reason and adaptive threshold fuzziness achieve the identification of fruit microscopic damage.It was verified that the present invention can have
Effect identifies various fruit appearance slight damages, and the correct recognition rata of identification test has reached more than 95%.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all at this
Any amendment, equivalent and the improvement etc. made within the spirit of invention and principle, should be included in this
Within the protection domain of invention.
Claims (5)
1. a fruit appearance detection method based on spectral image analysis, it is characterised in that include step:
The graphics standard calculating each wave band of spectrum picture is poorThe graphics standard selected is poorMaximum a
Spectral band;
Calculate any correlation coefficient between wave band vector o and wave band vector p in above-mentioned a spectral band
Rop, selects b minimum wave band vector of coefficient R op as preliminary characteristic wavelength;
Utilize Spectral angle mapping method, above-mentioned b wave band vector calculates treating of not damaged surface respectively
Spectrum between the spectrum picture c detecting fruit and the spectrum picture d of the fruit to be detected having injured surface
Angle β, is defined as fruit to be detected by the band combination that spectrum angle β is maximum and carries out spectral image analysis inspection
The characteristic wavelength used during survey;
Spectrum picture acquisition system utilizes characteristic wavelength to detect fruit outer surface, it is achieved fruit appearance is slightly damaged
Hinder the detection identification in region;
Wherein, a and b is integer and b < a.
Fruit appearance detection method based on spectral image analysis, its feature the most according to claim 1
Being, graphics standard is poorBe calculated as:
M and N represents line number and the columns of certain wave band respectively, and (i is j) that certain wave band is in spectrum picture to f
(m is certain wave band average gray value of all pixels in spectrum picture to pixel for i, gray value j).
Fruit appearance detection method based on spectral image analysis, its feature the most according to claim 1
Being, coefficient R op is calculated as:
moAnd mpIt is wave band o and the mean value vector of wave band p, x respectivelyoAnd xpRespectively represent wave band o and
The gray value of pixel, 0≤Rop≤1 on wave band p.
Fruit appearance detection method based on spectral image analysis, its feature the most according to claim 1
Being, spectrum angle β is calculated as:
xckAnd xdkIt is respectively two spectrum picture c and spectrum picture d spectral value on wave band k,
K=1,2 ..., b.
Fruit appearance detection method based on spectral image analysis, its feature the most according to claim 1
It is, b=6.
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CN107505325B (en) * | 2017-08-18 | 2023-04-25 | 西北农林科技大学 | Omnibearing quality detection method for winter jujube fruits |
CN109272030A (en) * | 2018-09-03 | 2019-01-25 | 贵阳学院 | Apple surface earlier damage quick nondestructive recognition methods based on fiber spectrum technology |
CN109270022B (en) * | 2018-09-14 | 2020-03-10 | 山东大学 | Waveband selection method of near-infrared spectrum model and model construction method |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101131734A (en) * | 2007-06-25 | 2008-02-27 | 北京航空航天大学 | Automatic waveband selection method for high optical spectrum remote sensing image |
WO2012060057A1 (en) * | 2010-11-01 | 2012-05-10 | 株式会社日立ハイテクノロジーズ | Defect inspection method, low light detecting method, and low light detector |
CN102495005A (en) * | 2011-11-17 | 2012-06-13 | 江苏大学 | Method for diagnosing crop water deficit through hyperspectral image technology |
CN102890092A (en) * | 2012-10-12 | 2013-01-23 | 浙江大学 | Characteristic angle cosine value method for detecting defects of honey peach brown rot |
CN103278464A (en) * | 2013-04-18 | 2013-09-04 | 北京工商大学 | Method and device for fish flesh detection |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5339963A (en) * | 1992-03-06 | 1994-08-23 | Agri-Tech, Incorporated | Method and apparatus for sorting objects by color |
-
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- 2014-09-05 CN CN201610404811.7A patent/CN105891230B/en active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101131734A (en) * | 2007-06-25 | 2008-02-27 | 北京航空航天大学 | Automatic waveband selection method for high optical spectrum remote sensing image |
WO2012060057A1 (en) * | 2010-11-01 | 2012-05-10 | 株式会社日立ハイテクノロジーズ | Defect inspection method, low light detecting method, and low light detector |
CN102495005A (en) * | 2011-11-17 | 2012-06-13 | 江苏大学 | Method for diagnosing crop water deficit through hyperspectral image technology |
CN102890092A (en) * | 2012-10-12 | 2013-01-23 | 浙江大学 | Characteristic angle cosine value method for detecting defects of honey peach brown rot |
CN103278464A (en) * | 2013-04-18 | 2013-09-04 | 北京工商大学 | Method and device for fish flesh detection |
Non-Patent Citations (4)
Title |
---|
B.SOMERS,ET AL: "Tree species mapping in tropical forests using multi-temporal imaging spectroscopy Wavelength adaptive spectral mixture analysis", 《INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION》 * |
NICOLA ACITO, ET AL: "Robust technique for anomalous change detection in airborne hyperspectral imagery based on automatic and adaptive band selection", 《OPTICAL ENGINEERING》 * |
赵春晖 等: "高光谱遥感图像最优波段选择方法的研究进展与分析", 《黑龙江大学自然科学学报》 * |
郭俊先 等: "基于高光谱成像技术的新疆冰糖心红富士苹果分级和糖度预测研究", 《新疆农业大学学报》 * |
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