CN110018164A - A kind of Fresh Grade Breast lignifying grade classification method and device based on three-dimensional imaging - Google Patents
A kind of Fresh Grade Breast lignifying grade classification method and device based on three-dimensional imaging Download PDFInfo
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- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
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- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
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- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F17/00—Methods or apparatus for determining the capacity of containers or cavities, or the volume of solid bodies
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/89—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles
- G01N21/8914—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles characterised by the material examined
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N2021/8411—Application to online plant, process monitoring
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N2021/845—Objects on a conveyor
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8854—Grading and classifying of flaws
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- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
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Abstract
The present invention relates to a kind of, and the Fresh Grade Breast lignifying grade based on three-dimensional imaging is classified method and device, and method specifically includes that the manually lignifying level ratings to Fresh Grade Breast；Fresh Grade Breast stereo-picture is acquired using binocular imaging principle, extracts five characteristic parameters of Fresh Grade Breast；By lignifying grade and five characteristic parameter corresponding records, Fresh Grade Breast lignifying rating database is established；BP neural network modeling method is pressed according to record, establishes the BP neural network model of Fresh Grade Breast degree of lignification；Lignifying classification is carried out to unrated Fresh Grade Breast according to the hierarchy model of foundation；The present invention realizes the automatic classification of Fresh Grade Breast lignifying grade in poultry meat processing, save grade on production line the employing of personnel, the corresponding spending of nursery, Fresh Grade Breast quality ratings standard is standardized, the objectivity, accuracy rate and working efficiency of Fresh Grade Breast Quality Detection are improved simultaneously, realizes online quality non-destructive testing classification in Fresh Grade Breast process.
Description
Technical field
The present invention relates to the detection and judgement of Fresh Grade Breast credit rating is related to, belong to processing of farm products and detection field, it is special
It is not a kind of Fresh Grade Breast lignifying grade classification method and device based on three-dimensional imaging.
Background technique
In recent years, bone-free Fresh Grade Breast is because its is full of nutrition, be easy to cook, the feature of price material benefit becomes consumer and likes
Meat products；Increase with market to bone-free Fresh Grade Breast demand, poultry farming enterprise constantly promote feeding efficiency, cultivate out
Meat rate is high, grows rapid broiler chicken kind.
Nowadays, broiler breeding rate compared with 50 years before shorten nearly half, and the liveweight of broiler chicken is that broiler chicken is living before 50 years
Twice of weight；However the popularization of poultry market fast-growing broiler chicken kind also brings a series of quality of poultry products with development
Problem, wherein the most urgent, maximum influence is wooden Fresh Grade Breast (Woody Breast, WB), production of poultry meat processing enterprise is every
Year is more than 200,000,000 U.S. dollars because of the economic loss that wooden Fresh Grade Breast generates.
The immediate cause for generating wooden meat, the quality of Fresh Grade Breast are not disclosed to the correlative study of wooden meat pathogenesis at present
Artificial finger pressure hardness determination method is taken in grade classification, but artificial finger pressure hardness rating method has the shortcomings that its is basic: first is that having
Subjectivity when different grading persons carries out lignifying ranking to identical Fresh Grade Breast, determines that there may be differences for result；
Second is that when same grading person carries out hardness level evaluation twice to identical Fresh Grade Breast, and its determining result there are inconsistency
It is also likely to be present difference；Third is that the grading of this method is at high cost, low efficiency.
Therefore, be badly in need of to Fresh Grade Breast lignifying grade classification methods and techniques further investigate, develop it is objective, accurate,
Efficient Fresh Grade Breast lignifying grade stage division.
Summary of the invention
It is an object of the invention to provide a kind of chicken based on three-dimensional imaging for deficiency present in above-mentioned background technique
Brisket lignifying grade is classified method and device.
To achieve the above object, the invention adopts the following technical scheme:
A kind of Fresh Grade Breast lignifying grade stage division based on three-dimensional imaging, comprising the following steps:
S1, it is manually graded by degree of lignification of the finger-press method to Fresh Grade Breast, rating scale is normal, slight, moderate, sternly
Four grades are weighed, respectively corresponds and is recorded using 0,1,2,3 four numerical value；
S2, the stereo-picture that Fresh Grade Breast is shot using binocular imaging principle, store the stereo image information of Fresh Grade Breast and carry out
Image procossing extracts length, width, thickness, area and the volume of Fresh Grade Breast under natural conditions；
S3, S1-S2 step is repeated, establishes Fresh Grade Breast lignifying rating database, every record of database is by same Fresh Grade Breast
Five characteristic parameters in S2, and the lignifying grade composition in S1；
S4, training set is used as to every record in Fresh Grade Breast lignifying rating database, with every five features recorded ginseng
Amount is input, is output with lignifying grade, according to BP neural network modeling method, establishes the BP mind of Fresh Grade Breast degree of lignification
Through network model；
S5, by the Fresh Grade Breast that do not graded manually under the identical shooting condition of S2, complete to believe the stereo-picture of Fresh Grade Breast
Breath acquisition, extracts three-dimensional Image Information Processing to obtain five characteristic parameters of Fresh Grade Breast, the Fresh Grade Breast then established according to S4
The lignifying grade of Fresh Grade Breast is calculated in the BP neural network model of degree of lignification.
It is non-integer since BP neural network model calculates resulting lignifying grade point, it is therefore desirable to wooden to Fresh Grade Breast
Change grade be rounded convenient for determining, and then the rounding method of the lignifying grade for the Fresh Grade Breast being calculated in the S5 are as follows:
The numerical value of resulting lignifying grade is calculated less than 0.5, lignifying grade takes 0；
The numerical value of resulting lignifying grade is calculated between 0.5 and 1 (including 1), lignifying grade takes 1；
The numerical value of resulting lignifying grade is calculated between 1 and 2 (including 2), lignifying grade takes 2；
The numerical value for calculating resulting lignifying grade is greater than 2, and lignifying grade takes 3.
Invention also provides a kind of Fresh Grade Breast lignifying grades based on three-dimensional imaging for realizing the above method
Grading plant, comprising:
Industrial personal computer；
Hardware module, the hardware module include the belt conveyor for transporting Fresh Grade Breast, are fixed on belt conveying collating unit
The fixation bracket of top, and it is installed on the binocular CCD industrial camera and trigger sensor of fixed bracket lower surface；And it is described
The conveyer belt of straight line and belt conveyor where the trigger point of trigger sensor and the shooting center of binocular CCD industrial camera is transported
Dynamic direction is vertical；
Solid space information extraction modules, which is installed in industrial personal computer, for handling binocular CCD
The shooting image of industrial camera extracts five characteristic parameters of Fresh Grade Breast；
Lignifying diversity module, the lignifying diversity module are installed in industrial personal computer, for according to solid space information extraction mould
Five characteristic parameters of the obtained Fresh Grade Breast of block carry out lignifying grading to Fresh Grade Breast.
Preferably, the shooting picture pixel size of the binocular CCD industrial camera is 480 × 640.
Preferably, the camera lens of binocular CCD industrial camera to belt conveyor conveyer belt vertical range
For 30cm.
Compared with prior art, beneficial effects of the present invention are as follows: it is wooden that the present invention realizes Fresh Grade Breast in poultry meat processing
The automatic classification for changing grade saves grade on production line the employing of personnel, the corresponding spending of nursery, has standardized pigeon breast meat
Matter rating scale, while the objectivity, accuracy rate and working efficiency of Fresh Grade Breast Quality Detection are improved, realize Fresh Grade Breast processing
Online quality non-destructive testing classification in the process.
The BP neural network model for the Fresh Grade Breast degree of lignification established in the present invention, for same class test object,
Binocular CCD industrial camera and trigger sensor type selecting and with the relative mounting location of conveyer belt it is constant under the premise of, can not be right
The hierarchy model is modified, so that it may suitable for the Fresh Grade Breast degree of lignification grading production line of different scales, be had extensive
Applicability；It, then can be according to the method for the present invention according to newly for different test object (such as Fresh Grade Breast age in days and kind are different)
Parameter is classified again after carrying out adaptivity adjustment to model.
Detailed description of the invention
Fig. 1 is the principle of the present invention block diagram；
Fig. 2 is the device of the invention figure；
Fig. 3 is the binocular stereo imaging effect diagram of Fresh Grade Breast；
Wherein: the fixed bracket of 1- binocular CDD industrial camera, 2- trigger sensor, 3-, 4- industrial personal computer, 5- Fresh Grade Breast, 6- belt are defeated
Send machine.
Specific embodiment
With reference to embodiment and attached drawing the present invention will be further described:
A kind of Fresh Grade Breast lignifying grade stage division based on three-dimensional imaging of the invention, comprising the following steps:
S1, it is manually graded by degree of lignification of the finger-press method to Fresh Grade Breast, rating scale is normal, slight, moderate, sternly
Four grades are weighed, respectively corresponds and is recorded using 0,1,2,3 four numerical value.
Specific artificial ranking process is as follows: by after centainly training and having research or the staff couple of grading experience
Fresh Grade Breast carries out artificial crawl observation degree of lignification grading, rating scale are as follows:
Normally, whole sufficiently flexible, smooth in appearance, exquisiteness, flexibility is good, weighs Fresh Grade Breast both ends in the hand in the hand and freely hangs down.
Slightly, whole soft, there is slight hardness sense of touch in meat sample apex zone.
Moderate, meat sample hardness is concentrated mainly on apex zone, but bottom is still soft, weigh in the hand bottom in the hand still have it is certain
Sagging sense, meat sample can integrally perceive hardness presence, but still have certain flexibility at middle part to bottom section, and bottom is hidden
Boss caused by consideration hardness increases.
Seriously, meat sample entirety sense of touch is hard and without flexibility, increases with scoring score, integral hardness increases, and surface is presented
Lignitoid's texture structure.Weigh in the hand in the hand without sagging sense, usually can integral vertical in the hand, there are moisture film shape secretion and bottom in entire body surface
Portion is evident that boss, and position sample surface different piece is with the presence of accessibility bloodstain.
Artificial rating scale provided in this embodiment is the routine evaluations standard in industry, standard that however, it is not limited to this,
Existing lignifying ranking method, which can be included in the method for the present invention, to be carried out using final unified wooden with 0,1,2,3 four
Change level value record.
S2, the stereo-picture that Fresh Grade Breast is shot using binocular imaging principle, store simultaneously the stereo image information of Fresh Grade Breast
Image procossing is carried out, length L, width W, thickness H, area S, the volume V of Fresh Grade Breast under natural conditions are extracted.
S3, S1-S2 step is repeated, establishes Fresh Grade Breast lignifying rating database, every record of database is by same chicken
Five characteristic parameters of the brisket in S2, and the lignifying grade composition in S1；
S4, training set is used as to every record in Fresh Grade Breast lignifying rating database, with every five features recorded ginseng
Amount is input, is output with lignifying grade, and according to BP neural network modeling method, establishing and entering and leaving node layer number is 5, hidden layer
Cautious number is 8, the BP neural network model that output layer number of nodes is 1.
The greatest iteration coefficient of the model is 2500 after parameters optimize, error performance target 0.002, minimum training
Rate is 0.1, dynamic parameter 0.3.
S5, by the Fresh Grade Breast that do not graded manually under the identical shooting condition of S2, complete to the perspective view of Fresh Grade Breast
As information collection, three-dimensional Image Information Processing is extracted to obtain five characteristic parameters of Fresh Grade Breast, the chicken then established according to S4
The lignifying grade of Fresh Grade Breast is calculated in the BP neural network model of brisket degree of lignification.
It is non-integer since BP neural network model calculates resulting lignifying grade point, it is therefore desirable to wooden to Fresh Grade Breast
Change grade be rounded convenient for determining, and then the rounding method of the lignifying grade for the Fresh Grade Breast being calculated in the S5 are as follows:
The numerical value of resulting lignifying grade is calculated less than 0.5, lignifying grade takes 0, i.e., normally；
The numerical value of resulting lignifying grade is calculated between 0.5 and 1 (including 1), lignifying grade takes 1, i.e., slightly；
The numerical value of resulting lignifying grade is calculated between 1 and 2 (including 2), lignifying grade takes 2, i.e. moderate；
The numerical value for calculating resulting lignifying grade is greater than 2, and lignifying grade takes 3, i.e., seriously.
The device that invention also provides a kind of specifically for realizing the method for the present invention.
The device principle block diagram is as shown in Figure 1, include industrial personal computer, hardware module, solid space information extraction modules and wood
Matter diversity module.
Wherein, industrial personal computer is as the control center of the whole device.
Hardware module is binocular stereo imaging device, mainly by belt conveyor, fixed bracket, trigger sensor, binocular
CCD industrial camera is constituted；Solid space information extraction modules are mounted in industrial personal computer, i.e., industrial personal computer will shoot obtained Fresh Grade Breast
Spatial image information handled, obtain length L, width W, thickness H, area S, the volume V of Fresh Grade Breast under natural conditions；Wood
Matter diversity module is similarly installed in industrial personal computer, the BP neural network model comprising Fresh Grade Breast degree of lignification, inputs pigeon breast
Five characteristic parameters of meat finally export normal, slight, moderate, serious, four Fresh Grade Breast lignifying grades.
Specific apparatus structure is as shown in Fig. 2, wherein industrial personal computer 4 is internally integrated solid space information extraction modules and wooden
Change diversity module, and controls the operation of belt conveyor 6.
Fixed bracket 3 is affixed to the top of belt conveyor 6, binocular CCD industrial camera two CCD industrial cameras of 1(,
By binocular imaging principle arrangement) and trigger sensor 2 be fixed on the lower surface of fixed bracket 3；Trigger sensor 2 and binocular CCD
It is appropriate that industrial camera 1 is needed in the position of fixed 3 lower surface of bracket, so that the trigger point of trigger sensor 2 and binocular CCD industry
The place straight line of the shooting central point of camera 1 is vertical with the conveying tape motion direction of belt conveyor 6, it is ensured that extracts stereo-picture
When Fresh Grade Breast 5 stay in, guarantee that Fresh Grade Breast 5 rests on the best shooting area of binocular CCD industrial camera 1.
Preferably, the shooting picture pixel size for choosing binocular CCD industrial camera 1 is 480 × 640, double
The camera lens of mesh CCD industrial camera 1 to belt conveyor 6 conveyer belt vertical range be 30cm, can accurately extract Fresh Grade Breast
Length, width, thickness and area under natural conditions, identification extract the precision of characteristic point up to 0.1mm.
When detection, Fresh Grade Breast 5 transports on the conveyer belt with belt conveyor 6, and transport to trigger sensor 2 detects pigeon breast
When meat 5, the acquisition and storage that binocular CCD industrial camera 1 completes 5 spatial image of Fresh Grade Breast are controlled by industrial personal computer 4, in industrial personal computer 4
Solid space information extraction modules the Fresh Grade Breast original spatial image of acquisition is handled after extract space characteristics point (such as
Shown in Fig. 3), it is when the quantity that space characteristics are selected is enough and is capable of the space characteristics of accurate description Fresh Grade Breast, i.e., extractable
Length L, width W, thickness H, area S, the volume V of Fresh Grade Breast under natural conditions.
In detection process, industrial personal computer 4 is controlled 6 short stay of belt conveyor 3 seconds and is shot for binocular CCD industrial camera 1, is clapped
The Fresh Grade Breast that industrial personal computer 4 controls that the transport of belt conveyor 6 has detected after the completion of taking the photograph leaves, while next Fresh Grade Breast being driven to enter
Detection.
The distance between position of two adjacent Fresh Grade Breast is preferably fixed value on conveyer belt, to be further ensured that Fresh Grade Breast
Position it is objective precisely.
Above embodiments and Figure of description are only that preferred embodiments of the present invention will be described, not to this hair
Bright range is defined, and without departing from the spirit of the design of the present invention, those of ordinary skill in the art are to of the invention
The various changes and improvements that technical solution is made should all be fallen into the protection scope that claims of the present invention determines.
Claims (5)
1. a kind of Fresh Grade Breast lignifying grade stage division based on three-dimensional imaging, it is characterised in that: the following steps are included:
S1, it is manually graded by degree of lignification of the finger-press method to Fresh Grade Breast, rating scale is normal, slight, moderate, sternly
Four grades are weighed, respectively corresponds and is recorded using 0,1,2,3 four numerical value；
S2, the stereo-picture that Fresh Grade Breast is shot using binocular imaging principle, store the stereo image information of Fresh Grade Breast and carry out
Image procossing extracts length, width, thickness, area and the volume of Fresh Grade Breast under natural conditions；
S3, S1-S2 step is repeated, establishes Fresh Grade Breast lignifying rating database, every record of database is by same Fresh Grade Breast
Five characteristic parameters in S2, and the lignifying grade composition in S1；
S4, training set is used as to every record in Fresh Grade Breast lignifying rating database, with every five features recorded ginseng
Amount is input, is output with lignifying grade, according to BP neural network modeling method, establishes the BP mind of Fresh Grade Breast degree of lignification
Through network model；
S5, by the Fresh Grade Breast that do not graded manually under the identical shooting condition of S2, complete to believe the stereo-picture of Fresh Grade Breast
Breath acquisition, extracts three-dimensional Image Information Processing to obtain five characteristic parameters of Fresh Grade Breast, the Fresh Grade Breast then established according to S4
The lignifying grade of Fresh Grade Breast is calculated in the BP neural network model of degree of lignification.
2. a kind of Fresh Grade Breast lignifying grade stage division based on three-dimensional imaging according to claim 1, feature exist
In: the rounding method of the lignifying grade for the Fresh Grade Breast being calculated in the S5 are as follows:
The numerical value of resulting lignifying grade is calculated less than 0.5, lignifying grade takes 0；
The numerical value of resulting lignifying grade is calculated between 0.5 and 1 (including 1), lignifying grade takes 1；
The numerical value of resulting lignifying grade is calculated between 1 and 2 (including 2), lignifying grade takes 2；
The numerical value for calculating resulting lignifying grade is greater than 2, and lignifying grade takes 3.
3. a kind of Fresh Grade Breast lignifying grade grading plant based on three-dimensional imaging, it is characterised in that: include:
Industrial personal computer；
Hardware module, the hardware module include the belt conveyor for transporting Fresh Grade Breast, are fixed on belt conveying collating unit
The fixation bracket of top, and it is installed on the binocular CCD industrial camera and trigger sensor of fixed bracket lower surface；And it is described
The conveyer belt of straight line and belt conveyor where the trigger point of trigger sensor and the shooting center of binocular CCD industrial camera is transported
Dynamic direction is vertical；
Solid space information extraction modules, which is installed in industrial personal computer, for handling binocular CCD
The shooting image of industrial camera extracts five characteristic parameters of Fresh Grade Breast；
Lignifying diversity module, the lignifying diversity module are installed in industrial personal computer, for according to solid space information extraction mould
Five characteristic parameters of the obtained Fresh Grade Breast of block carry out lignifying grading to Fresh Grade Breast.
4. a kind of Fresh Grade Breast lignifying grade grading plant based on three-dimensional imaging according to claim 3, feature exist
In: the shooting picture pixel size of the binocular CCD industrial camera is 480 × 640.
5. a kind of Fresh Grade Breast lignifying grade grading plant based on three-dimensional imaging according to claim 3, feature exist
In: the vertical range of the conveyer belt of the camera lens of binocular CCD industrial camera to belt conveyor is 30cm.
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| CN110702015B (en) * | 2019-09-26 | 2021-09-03 | 中国南方电网有限责任公司超高压输电公司曲靖局 | Method and device for measuring icing thickness of power transmission line |
| CN113358705A (en) * | 2021-05-25 | 2021-09-07 | 南京农业大学 | Chicken breast lignification grading method based on bioelectrical impedance technology |
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