CN109377485A - A kind of instant noodles packaging defect machine vision detection method - Google Patents

A kind of instant noodles packaging defect machine vision detection method Download PDF

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CN109377485A
CN109377485A CN201811188773.1A CN201811188773A CN109377485A CN 109377485 A CN109377485 A CN 109377485A CN 201811188773 A CN201811188773 A CN 201811188773A CN 109377485 A CN109377485 A CN 109377485A
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image
instant noodles
defect
packaging
gray
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CN109377485B (en
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张永宾
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Shenzhen Zhongzhi Vision Technology Co Ltd
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Long Mei Mei Si Environmental Protection Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/40—Image enhancement or restoration by the use of histogram techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20076—Probabilistic image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30128—Food products
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Abstract

The invention discloses a kind of instant noodles to pack defect machine vision detection method, specifically includes that and establishes instant noodles packaging automatic defect detecting device model, acquires the instant noodles Packaging image on conveyer belt, pre-process to image, enhance characteristics of image；Image segmentation central pixel point is chosen, similarity measures are established according to the continuity between pixel grey scale, all pixels are divided into different zones；Packaging image is subjected to convolution with Gaussian kernel function and obtains difference of Gaussian image, by asking the gray scale extreme value of image to extract the defect characteristic of instant noodles Packaging image；Classified by measuring the similitude between defect characteristic and sample, detect defect classification and kick and remove, completes the automatic detection of instant noodles packaging defect.This method has preferable stability and robustness, reduces omission factor and false detection rate, improves detection efficiency, and detection speed is fast, realizes non-destructive testing, saves human resources, is enterprise's reduced cost, refines instant noodles packaging more.

Description

A kind of instant noodles packaging defect machine vision detection method
Technical field
The present invention relates to food quality inspection, image recognition, art of mathematics, and in particular to a kind of instant noodles packaging defect machine view Feel detection method.
Background technique
Instant noodles packaging all has a very big impact the appearance and quality of instant noodles, inevitably deposits in print production In various defects or flaw.More significantly, detection efficiency is low for the labor intensity that existing instant noodles packaging defect detecting device needs, Easily there is erroneous detection and detection leakage phenomenon in bad adaptability, and testing cost is higher, is not conducive to the reduction of production costs, and weakens instant noodles Competitiveness.Enterprise without on-line measuring device can only be by way of manually spot-check, and defect detection rate is very low.
Summary of the invention
To solve the above problems, the purpose of the present invention is to provide a kind of convenience with preferable stability and robustness Bread fills defect machine vision detection method, reduces omission factor and false detection rate, improves detection efficiency, and detection speed is fast, realizes nothing Damage detection, saves human resources, is enterprise's reduced cost, refines instant noodles packaging more.
The present invention solves the problems, such as technical solution used by it, comprising the following steps:
A. instant noodles packaging automatic defect detecting device model is established, the instant noodles Packaging image on conveyer belt is acquired, it is right Image is pre-processed, and characteristics of image is enhanced；
B. image segmentation central pixel point is chosen, similarity measures are established according to the continuity between pixel grey scale, it will All pixels are divided into different zones；
C. Packaging image is subjected to convolution with Gaussian kernel function and obtains difference of Gaussian image, by the gray scale pole for seeking image Value extracts the defect characteristic of instant noodles Packaging image；
D. classified by measuring the similitude between defect characteristic and sample, detect defect classification and kick and remove, complete The automatic detection of instant noodles packaging defect.
Further, the step A includes:
(1) instant noodles packaging automatic defect detecting device is established, camera acquires the instant noodles Packaging image on conveyer belt, It sends master control set to, and image is digitized, equilibrium treatment carried out to the gray value of image using histogram, at equilibrium Image grayscale after reason are as follows:
Wherein, I is the gray level of image, and f (t) is the probability density function of image grayscale, and t is pixel, to image ash Degree grade is normalized, then when l ∈ [0,1], probability density function values 1；It otherwise is 0；
(2) if the gray value of image slices vegetarian refreshments (x, y) is g (x, y), tonal range is [g1,g2], it will by mapping function Gray value is mapped as g'(x, y), carry out greyscale transformation:
Wherein, (α, β) is the tonal range after greyscale transformation.
Further, the step B includes:
(1) to facilitating bread dress image to carry out color space conversion, image is carried out by initialization point by dividing ridge method It cuts, if some region is Vr, neighborhood Vi(i=1,2 ..., n), then similarity function are as follows:
Wherein, ω is weight coefficient, and n is neighborhood quantity, xiIt is the gray average in each region,It is the ash of all areas Mean value is spent, obtains a threshold value by maximum variance between clusters, similarity function value is greater than this threshold value, which is chosen as planting Subregion；
(3) seed region of selection is marked, traverses all seed regions and its neighborhood, if there is region not marked Note, checks the neighborhood in the region, if neighbourhood signatures are identical, which is similarly marked；If neighbourhood signatures are different, count The gray average for calculating the region and neighborhood is poor, and the smallest neighborhood of selection differences carries out identical label, traverses all areas, until All areas are labeled, so that the adjacent pixel for having similar gray-value with seed region is just merged into a region, divide The scrappy zonule in many of region, setting regions range threshold is poor to gray average by the region merging technique for being less than threshold value In different the smallest neighborhood, to complete image segmentation.
Further, the step C includes:
(1) Gaussian function of instant noodles Packaging image is constructed:
Wherein, σ is the standard deviation of image normal distribution, and (x, y) is pixel coordinate, (x0,y0) be seed point coordinate, The then scale space function of image I (x, y) are as follows:
S (x, y)=f (x, y) * I (x, y)
Wherein, * indicates convolution, then σ is smaller, and graphical rule is smaller, and minutia is also just more obvious；
(2) Packaging image generates one group of image by Gaussian function convolution, is divided in scale space by constant k From obtaining difference of Gaussian function:
D (x, y)=[f (x, y)-f (k (x, y))] * I (x, y)
=S (k (x, y))-S (x, y)
The pixel in image is compared by difference of Gaussian function, sets gray threshold, gray value is found and is greater than The pixel of threshold value is found gray scale extreme point as characteristic point to be selected in residual pixel point, difference of Gaussian function is carried out safe Strangle expansion:
Wherein, (x, y)TIt is the offset of pixel, enablesIt, will be in candidate feature point to obtain extreme point Pixel positioned at edge removes, and extracts stable characteristic point.
Further, the step D includes:
Calculate the Euclidean distance between the characteristic image f (x, y) extracted and detection sample s (x, y):
Wherein, fi(x, y) is ith feature, and n is feature quantity, finds the smallest feature min (d) of Euclidean distance and carries out Classification, master control set statistical shortcomings type and sending a signal to are kicked except device kick removing, under not having defective packaging that will continue Procedure, to complete the automatic detection of instant noodles packaging defect.
The beneficial effects of the present invention are:
In the case where instant noodles packaging quality requires higher and higher, the present invention has preferable stability and robustness, Omission factor and false detection rate are reduced, detection efficiency is improved, detection speed is fast, realizes non-destructive testing, saves human resources, is enterprise Reduced cost refines instant noodles packaging more.
Detailed description of the invention
Fig. 1 is the overall flow figure that a kind of instant noodles pack defect machine vision detection method；
Fig. 2 fills automatic defect detecting device illustraton of model for convenience of bread；
Fig. 3 fills image deflects feature extraction flow chart for convenience of bread.
Specific embodiment
Referring to Fig.1, method of the present invention the following steps are included:
A. instant noodles packaging automatic defect detecting device model is established, the instant noodles Packaging image on conveyer belt is acquired, it is right Image is pre-processed, and characteristics of image is enhanced；
(1) instant noodles packaging automatic defect detecting device is established, as shown in Figure 2.Camera acquires the convenience on conveyer belt Face Packaging image, sends master control set to.There may be various noises for the original image of collection in worksite, it is therefore desirable to be located in advance Reason.Image is digitized, equilibrium treatment is carried out using gray value of the histogram to image, enhances visual effect.At equilibrium Image grayscale after reason are as follows:
Wherein, I is the gray level of image, and f (t) is the probability density function of image grayscale, and t is pixel.To image ash Degree grade is normalized, then when l ∈ [0,1], probability density function values 1；It otherwise is 0.
(2) if the gray value of image slices vegetarian refreshments (x, y) is g (x, y), tonal range is [g1,g2], it will by mapping function Gray value is mapped as g'(x, y), carry out greyscale transformation:
Wherein, (α, β) is the tonal range after greyscale transformation.To expand the tonal range of image, keep image more clear It is clear.
B. image segmentation central pixel point is chosen, similarity measures are established according to the continuity between pixel grey scale, it will All pixels are divided into different zones；
(1) to facilitating bread dress image to carry out color space conversion, image is carried out by initialization point by dividing ridge method It cuts.If some region is Vr, neighborhood Vi(i=1,2 ..., n), then similarity function are as follows:
Wherein, ω is weight coefficient, and n is neighborhood quantity, xiIt is the gray average in each region,It is the ash of all areas Spend mean value.A threshold value is obtained by maximum variance between clusters, similarity function value is greater than this threshold value, which is chosen as planting Subregion.
(2) seed region of selection is marked, traverses all seed regions and its neighborhood.If there is region not marked Note, checks the neighborhood in the region, if neighbourhood signatures are identical, which is similarly marked；If neighbourhood signatures are different, count The gray average for calculating the region and neighborhood is poor, and the smallest neighborhood of selection differences carries out identical label.All areas are traversed, until All areas are labeled.To which the adjacent pixel for having similar gray-value with seed region is just merged into a region.Segmentation The scrappy zonule in many of region, setting regions range threshold is poor to gray average by the region merging technique for being less than threshold value In different the smallest neighborhood, to complete image segmentation.
C. Packaging image is subjected to convolution with Gaussian kernel function and obtains difference of Gaussian image, by the gray scale pole for seeking image Value extracts the defect characteristic (as shown in Figure 3) of instant noodles Packaging image；
(1) Gaussian function of instant noodles Packaging image is constructed:
Wherein, σ is the standard deviation of image normal distribution, and (x, y) is pixel coordinate, (x0,y0) be seed point coordinate. The then scale space function of image I (x, y) are as follows:
S (x, y)=f (x, y) * I (x, y)
Wherein, * indicates convolution.Then σ is smaller, and graphical rule is smaller, and minutia is also just more obvious.
(2) Packaging image generates one group of image by Gaussian function convolution, is divided in scale space by constant k From obtaining difference of Gaussian function:
D (x, y)=[f (x, y)-f (k (x, y))] * I (x, y)
=S (k (x, y))-S (x, y)
The pixel in image is compared by difference of Gaussian function, sets gray threshold, gray value is found and is greater than The pixel of threshold value is found gray scale extreme point as characteristic point to be selected in residual pixel point, difference of Gaussian function is carried out safe Strangle expansion:
Wherein, (x, y)TIt is the offset of pixel.It enablesTo obtain extreme point.It will be in candidate feature point Pixel positioned at edge removes, and extracts stable characteristic point.
D. classified by measuring the similitude between defect characteristic and sample, detect defect classification and kick and remove, complete The automatic detection of instant noodles packaging defect.
Calculate the Euclidean distance between the characteristic image f (x, y) extracted and detection sample s (x, y):
Wherein, fi(x, y) is ith feature, and n is feature quantity.The smallest feature min (d) of Euclidean distance is found to carry out Classification, master control set statistical shortcomings type and sending a signal to are kicked except device kick removing, under not having defective packaging that will continue Procedure, to complete the automatic detection of instant noodles packaging defect.
In conclusion just completing a kind of instant noodles packaging defect machine vision detection method of the present invention.The party Method has preferable stability and robustness, reduces omission factor and false detection rate, improves detection efficiency, and detection speed is fast, realizes nothing Damage detection, saves human resources, is enterprise's reduced cost, refines instant noodles packaging more.

Claims (5)

1. a kind of instant noodles pack defect machine vision detection method, which comprises the following steps:
A. instant noodles packaging automatic defect detecting device model is established, the instant noodles Packaging image on conveyer belt is acquired, to image It is pre-processed, enhances characteristics of image；
B. image segmentation central pixel point is chosen, similarity measures are established according to the continuity between pixel grey scale, will be owned Pixel is divided into different zones；
C. Packaging image is subjected to convolution with Gaussian kernel function and obtains difference of Gaussian image, by asking the gray scale extreme value of image to mention Take the defect characteristic of instant noodles Packaging image；
D. classified by measuring the similitude between defect characteristic and sample, detect defect classification and kick and remove, it is convenient to complete The automatic detection of bread dress defect.
2. instant noodles as described in claim 1 pack defect machine vision detection method, which is characterized in that the step A packet It includes:
(1) instant noodles packaging automatic defect detecting device is established, camera acquires the instant noodles Packaging image on conveyer belt, transmission It is digitized to master control set, and to image, equilibrium treatment is carried out to the gray value of image using histogram, after equilibrium treatment Image grayscale are as follows:
Wherein, I is the gray level of image, and f (t) is the probability density function of image grayscale, and t is pixel, to image gray levels It is normalized, then when l ∈ [0,1], probability density function values 1；It otherwise is 0；
(2) if the gray value of image slices vegetarian refreshments (x, y) is g (x, y), tonal range is [g1,g2], by mapping function by gray scale Value is mapped as g'(x, y), carry out greyscale transformation:
Wherein, (α, β) is the tonal range after greyscale transformation.
3. instant noodles as claimed in claim 2 pack defect machine vision detection method, which is characterized in that the step B packet It includes:
(1) to facilitating bread dress image to carry out color space conversion, image is carried out by initialization segmentation by dividing ridge method, if Some region is Vr, neighborhood Vi(i=1,2 ..., n), then similarity function are as follows:
Wherein, ω is weight coefficient, and n is neighborhood quantity, xiIt is the gray average in each region,Be all areas gray scale it is equal Value obtains a threshold value by maximum variance between clusters, and similarity function value is greater than this threshold value, which is chosen as seed zone Domain；
(2) seed region of selection is marked, traverses all seed regions and its neighborhood, if there is region not to be labeled, looked into See the neighborhood in the region, if neighbourhood signatures are identical, which is similarly marked；If neighbourhood signatures are different, calculating should The gray average of region and neighborhood is poor, and the smallest neighborhood of selection differences carries out identical label, traverses all areas, until all Region is labeled, so that the adjacent pixel for having similar gray-value with seed region is just merged into a region, the area of segmentation The scrappy zonule in many of domain, setting regions range threshold, most to gray average difference by the region merging technique less than threshold value In small neighborhood, to complete image segmentation.
4. instant noodles as claimed in claim 3 pack defect machine vision detection method, which is characterized in that the step C packet It includes:
(1) Gaussian function of instant noodles Packaging image is constructed:
Wherein, σ is the standard deviation of image normal distribution, and (x, y) is pixel coordinate, (x0,y0) be seed point coordinate, then scheme As the scale space function of I (x, y) are as follows:
S (x, y)=f (x, y) * I (x, y)
Wherein, * indicates convolution, then σ is smaller, and graphical rule is smaller, and minutia is also just more obvious；
(2) Packaging image generates one group of image by Gaussian function convolution, is separated, is obtained by constant k in scale space To difference of Gaussian function:
D (x, y)=[f (x, y)-f (k (x, y))] I (x, y)
=S (k (x, y))-S (x, y)
The pixel in image is compared by difference of Gaussian function, sets gray threshold, gray value is found and is greater than threshold value Pixel, gray scale extreme point is found in residual pixel point as characteristic point to be selected, difference of Gaussian function is subjected to Taylor's exhibition It opens:
Wherein, (x, y)TIt is the offset of pixel, enablesTo obtain extreme point, will be located in candidate feature point The pixel of edge removes, and extracts stable characteristic point.
5. instant noodles as claimed in claim 4 pack defect machine vision detection method, which is characterized in that the step D packet It includes:
Calculate the Euclidean distance between the characteristic image f (x, y) extracted and detection sample s (x, y):
Wherein, fi(x, y) is ith feature, and n is feature quantity, finds the smallest feature min (d) of Euclidean distance and classifies, Master control set statistical shortcomings type and sending a signal to is kicked except device kick removing, and does not have defective packaging that will continue lower road work Sequence, to complete the automatic detection of instant noodles packaging defect.
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