CN109470708A - Quality determining method, device, server and the storage medium of plastic foam cutlery box - Google Patents
Quality determining method, device, server and the storage medium of plastic foam cutlery box Download PDFInfo
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- CN109470708A CN109470708A CN201811458882.0A CN201811458882A CN109470708A CN 109470708 A CN109470708 A CN 109470708A CN 201811458882 A CN201811458882 A CN 201811458882A CN 109470708 A CN109470708 A CN 109470708A
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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
-
- 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
-
- 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
- G01N2021/8883—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 involving the calculation of gauges, generating models
-
- 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
- G01N2021/8887—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
Abstract
The embodiment of the invention discloses quality determining method, device, server and the storage mediums of a kind of plastic foam cutlery box.The described method includes: obtaining the image data of plastic foam cutlery box to be detected；The image data of the foam cutlery box to be detected is converted to the quality testing request of the plastic foam cutlery box to be detected；Quality testing request is sent to the server for being deployed with the model of the classification and Detection based on picture classification；It receives the server and corresponding quality measurements is requested by the quality testing classification of the classification and Detection model output based on picture classification.The detection efficiency of plastic foam cutlery box can be not only reduced, but also the detection accuracy of plastic foam cutlery box can be improved.
Description
Technical field
The present embodiments relate to field of artificial intelligence more particularly to a kind of quality testing sides of plastic foam cutlery box
Method, device, server and storage medium.
Background technique
In the production process of traditional manufacture, quality inspection is the key link in production procedure.Specifically, in plastic foam
In the production process of cutlery box, a kind of important means detected to the quality of plastic foam cutlery box is to plastic foam cutlery box
Surface state is detected, to judge that plastic foam cutlery box whether there is flaw and defect, and according to testing result to plastic blister
Foam cutlery box does corresponding processing.The detection method of existing plastic foam cutlery box includes following two: first method is pure artificial
Quality inspection mode, dependent on the experience of quality inspection personnel, quality inspection personnel, which visually observes the appearance photo of product and then empirically provides, to be sentenced
It is disconnected；Second method is the semi-automatic quality inspection mode of machine auxiliary, is mainly filtered out by the quality inspection system with certain judgement
Do not have defective photo, detection judgement is then carried out by photo of the quality inspection personnel to doubtful existing defects again.
But for the first quality inspection mode and second of quality inspection mode, the process of artificial quality inspection is all referred to, quality inspection is needed
Personnel carry out walkaround inspection in production scene, and manual record gets off to do subsequent processing again after finding defect.This method is not only
Low efficiency is easy erroneous judgement of failing to judge, and data are difficult to carry out secondary use excavation, and industrial production environment is often relatively more severe, right
Quality inspection personnel or the health and safety of producers will cause adverse effect.For second of quality detection mode, due to its quality inspection
Examining system is developed in the quality inspection system based on traditional expert system or Feature Engineering, and detection decision rule is all based on
Experience is cured in machine, it is difficult to the development iteration of business, lead to the detection of the development quality inspection system with production technology
Precision is lower and lower, or even is reduced to complete unusable state.In addition, the detection decision rule of traditional quality inspection system is all by the
Tripartite supplier solidifies within hardware in advance, and when upgrading not only needs to carry out key technological transformation to production line, but also expensive.It passes
Unite quality inspection system safety, standardization, in terms of all there is obvious deficiencies, be unfavorable for traditional industry production line
Optimization and upgrading.
Summary of the invention
In view of this, the embodiment of the present invention provide the quality determining method of plastic foam cutlery box a kind of, device, server and
Storage medium, can not only reduce the detection efficiency of plastic foam cutlery box, but also the detection of plastic foam cutlery box can be improved
Accuracy.
In a first aspect, the embodiment of the invention provides a kind of quality determining method of plastic foam cutlery box, the method packet
It includes:
Obtain the image data of plastic foam cutlery box to be detected；
The image data of the foam cutlery box to be detected is converted to the quality of the plastic foam cutlery box to be detected
Detection request；
Quality testing request is sent to the server for being deployed with the model of the classification and Detection based on picture classification；
It receives the server and passes through the quality testing point of the classification and Detection model output based on picture classification
Class requests corresponding quality measurements.
In the above-described embodiments, described that quality testing request is sent to the classification inspection being deployed with based on picture classification
Survey the server of model, comprising:
Deployment is obtained in pre-set server configuration table is stated the classification and Detection model based on picture classification
The load condition of each server；
According to the load condition for each server for being deployed with the classification and Detection model, the quality testing is requested to send out
It send to the smallest server of load for being deployed with the classification and Detection model based on picture classification.
In the above-described embodiments, the method also includes:
According to the corresponding relationship of pre-set quality measurements and defect processing operation, the quality testing knot is made
The corresponding defect processing operation of fruit；Wherein, the defect processing operation includes: that alarm, shutdown, storage log or control are mechanical
Arm.
Second aspect, the embodiment of the invention provides the quality determining method of another plastic foam cutlery box, the methods
Include:
Receive the quality testing request of plastic foam cutlery box to be detected；
The quality testing, which is calculated, by the classification and Detection model based on picture classification of training in advance requests corresponding matter
Measure testing result.
In the above-described embodiments, the classification and Detection model based on picture classification include: depth convolutional neural networks and
Defect location sorter network；Wherein, the depth convolutional neural networks are used to extract the feature of steel plate picture, and by the feature
It is input in the defect location sorter network；The defect location sorter network is calculated for the classification and Detection based on picture classification
Method judge the extracted feature of depth convolutional neural networks whether existing defects, and obtain the classification of the defect.
In the above-described embodiments, the depth convolutional neural networks include: convolutional layer, pond layer and full articulamentum；Wherein,
The convolutional layer, the convolution kernel for utilizing weight different is to the plastic foam to be detected in quality testing request
The image of cutlery box is scanned convolution, therefrom extracts the feature of various meanings, and export into characteristic pattern；The pond layer is used
In to characteristic pattern progress dimensionality reduction operation；The full articulamentum, for the Feature Mapping extracted to be classified to the defect location
In network.
The third aspect, the embodiment of the invention provides a kind of quality detection device of plastic foam cutlery box, described device packets
It includes: image collection module, image conversion module, request sending module and result receiving module；Wherein,
Described image obtains module, for obtaining the image data of plastic foam cutlery box to be detected；
Described image conversion module, it is described to be detected for being converted to the image data of the foam cutlery box to be detected
Plastic foam cutlery box quality testing request；
The request sending module is deployed with the classification based on picture classification for quality testing request to be sent to
The server of detection model；
The result receiving module passes through the classification and Detection model based on picture classification for receiving the server
Corresponding quality measurements are requested in the quality testing classification of output.
In the above-described embodiments, the request sending module, specifically for being obtained in pre-set server configuration table
Take the load condition for being deployed with each server of the classification and Detection model based on picture classification；According to being deployed with described point
Quality testing request is sent to described in being deployed with based on picture point by the load condition of each server of class detection model
The smallest server of load of the classification and Detection model of class.
In the above-described embodiments, described device further include: operation processing module, for according to pre-set quality testing
As a result with the corresponding relationship of defect processing operation, the corresponding defect processing operation of the quality measurements is made；Wherein, described
Defect processing operation includes: alarm, shutdown, storage log or control mechanical arm.
Fourth aspect, the embodiment of the invention provides the quality detection device of another plastic foam cutlery box, described devices
It include: request receiving module and result computing module；Wherein,
The request receiving module, the quality testing for receiving plastic foam cutlery box to be detected are requested；
The result computing module, described in being calculated by the classification and Detection model based on picture classification of training in advance
Corresponding quality measurements are requested in quality testing.
In the above-described embodiments, the classification and Detection model based on picture classification include: depth convolutional neural networks and
Defect location sorter network；Wherein, the depth convolutional neural networks are used to extract the feature of steel plate picture, and by the feature
It is input in the defect location sorter network；The defect location sorter network is calculated for the classification and Detection based on picture classification
Method judge the extracted feature of depth convolutional neural networks whether existing defects, and obtain the classification of the defect.
In the above-described embodiments, the depth convolutional neural networks include: convolutional layer, pond layer and full articulamentum；Wherein,
The convolutional layer, the convolution kernel for utilizing weight different is to the plastic foam to be detected in quality testing request
The image of cutlery box is scanned convolution, therefrom extracts the feature of various meanings, and export into characteristic pattern；The pond layer is used
In to characteristic pattern progress dimensionality reduction operation；The full articulamentum, for the Feature Mapping extracted to be classified to the defect location
In network.
5th aspect, the embodiment of the invention provides a kind of servers, comprising:
One or more processors；
Memory, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processing
Device realizes the quality determining method of plastic foam cutlery box described in any embodiment of that present invention.
6th aspect, the embodiment of the invention provides a kind of storage mediums, are stored thereon with computer program, the program quilt
The quality determining method of plastic foam cutlery box described in any embodiment of that present invention is realized when processor executes.
Quality determining method, device, server and the storage medium for the plastic foam cutlery box that the embodiment of the present invention proposes, first
Obtain the image data of plastic foam cutlery box to be detected；Then the image data of foam cutlery box to be detected is converted to be checked
The quality testing of the plastic foam cutlery box of survey is requested；Quality testing request is sent to again and is deployed with the classification based on picture classification
The server of detection model；The server calculates quality testing by the classification and Detection model based on picture classification of training in advance
Request corresponding quality measurements.That is, in the inventive solutions, server by training in advance based on
The classification and Detection model of picture classification carries out quality testing to plastic foam cutlery box to be detected.And in existing two kinds of plastic blisters
In the quality determining method of foam cutlery box, first method is pure artificial quality inspection mode, dependent on the experience of quality inspection personnel, quality inspection personnel
It visually observes the appearance photo of product and then empirically provides judgement；Second method is the semi-automatic quality inspection mode of machine auxiliary,
It is mainly filtered out by the quality inspection system with certain judgement and does not have defective photo, then deposited again by quality inspection personnel to doubtful
Detection judgement is carried out in the photo of defect.Therefore, compared to the prior art, the plastic foam cutlery box that the embodiment of the present invention proposes
Quality determining method, device, server and storage medium can not only reduce the detection efficiency of plastic foam cutlery box, but also
The detection accuracy of plastic foam cutlery box can be improved；Also, the technical solution realization of the embodiment of the present invention is simple and convenient, is convenient for
Universal, the scope of application is wider.
Detailed description of the invention
Fig. 1 is the flow diagram of the quality determining method for the plastic foam cutlery box that the embodiment of the present invention one provides；
Fig. 2 is the flow diagram of the quality determining method of plastic foam cutlery box provided by Embodiment 2 of the present invention；
Fig. 3 is the flow diagram of the quality determining method for the plastic foam cutlery box that the embodiment of the present invention three provides；
Fig. 4 is the schematic illustration for the classification and Detection model based on picture classification that the embodiment of the present invention three provides；
Fig. 5 is the schematic illustration for the depth convolutional neural networks that the embodiment of the present invention three provides；
Fig. 6 is the structural schematic diagram for the detection system that the embodiment of the present invention three provides；
Fig. 7 is the structural schematic diagram of the quality detection device for the plastic foam cutlery box that the embodiment of the present invention four provides；
Fig. 8 is the structural schematic diagram of the quality detection device for the plastic foam cutlery box that the embodiment of the present invention five provides；
Fig. 9 is the structural schematic diagram for the server that the embodiment of the present invention six provides.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining the present invention rather than limiting the invention.It also should be noted that in order to just
In description, only some but not all contents related to the present invention are shown in the drawings.
In the prior art, disposable snack box is increasingly turned to environmentally friendly lunch box by foam food boxes, papery foam originally
Lunch box is due to non-refractory, and manufacturing process damages environment and is eliminated, and takes and to it the plastic lunch box that has, meal made of paper
Box, wooden lunch box, degradation lunch box etc..Wherein, plastics are with toxicity is lower, fusing point is higher, plasticity is strong, production is easy and opposite
The features such as cost is relatively low, because having formed the mainstay material of manufacture disposable snack box.The embodiment of the present invention proposes a kind of plastics
The quality determining method of foam cutlery box, the classification and Detection model based on picture classification carry out the surface state of plastic foam cutlery box
Quality testing.The quality determining method of the plastic foam cutlery box based on picture classification is described in detail below.
Embodiment one
Fig. 1 is the flow diagram of the quality determining method for the plastic foam cutlery box that the embodiment of the present invention one provides, the party
Method can by plastic foam cutlery box quality detection device perhaps server come execute the device or server can be by software
And/or the mode of hardware is realized, the device or server can integrate in any smart machine with network communicating function
In.As shown in Figure 1, the quality determining method of plastic foam cutlery box may comprise steps of:
S101, the image data for obtaining plastic foam cutlery box to be detected.
It in a specific embodiment of the present invention, can be with when the surface state to plastic foam cutlery box carries out quality testing
The image data of plastic foam cutlery box to be detected is obtained by the equipment such as video camera.Since plastic foam cutlery box was producing
Yield in journey is larger, and cost is relatively low, and reproducibility is stronger, therefore, in the picture number for obtaining plastic foam cutlery box to be detected
According to when, the image data of plastic foam cutlery box to be detected can be obtained according to preset collection period.It can drop in this way
The amount of calculation of the server of the low classification and Detection model based on picture classification.
S102, the quality testing that the image data of foam cutlery box to be detected is converted to plastic foam cutlery box to be detected
Request.
In a specific embodiment of the present invention, when the surface state to plastic foam cutlery box carries out quality testing, pass through
It, can be by foam cutlery box to be detected after the equipment such as video camera gets the image data of plastic foam cutlery box to be detected
Image data be converted to plastic foam cutlery box to be detected quality testing request.It specifically, can be according to predetermined
Format transformation requests the quality testing that the image data of foam cutlery box to be detected is converted to plastic foam cutlery box to be detected.
For example, the quality testing request of plastic foam cutlery box to be detected may include: quality testing order and foam to be detected meal
The image data of box.
S103, quality testing request is sent to the server for being deployed with the model of the classification and Detection based on picture classification.
It in a specific embodiment of the present invention, can be first to the deployment of detection model before sending quality testing request
Quality testing request is sent to and is deployed with based on picture classification by the case where situation is monitored, and is disposed according to detection model
The server of classification and Detection model.
S104, the quality testing classification request pair that server is exported by the classification and Detection model based on picture classification is received
The quality measurements answered.
In a specific embodiment of the present invention, the server for being deployed with the classification and Detection model based on picture classification can lead to
The classification and Detection model based on picture classification after training in advance calculates the corresponding quality measurements of quality testing request.Cause
This can receive server and classified by the quality testing that the classification and Detection model based on picture classification exports in this step
Request corresponding quality measurements.
The quality determining method for the plastic foam cutlery box that the embodiment of the present invention proposes first obtains plastic foam meal to be detected
The image data of box；Then the image data of foam cutlery box to be detected is converted to the quality of plastic foam cutlery box to be detected
Detection request；Quality testing request is sent to the server for being deployed with the model of the classification and Detection based on picture classification again；The clothes
Business device calculates quality testing by the classification and Detection model based on picture classification of training in advance and requests corresponding quality testing knot
Fruit.That is, in the inventive solutions, the classification and Detection mould based on picture classification that server passes through training in advance
Type carries out quality testing to plastic foam cutlery box to be detected.And in the quality determining method of existing two kinds of plastic foam cutlery boxs
In, first method is pure artificial quality inspection mode, and dependent on the experience of quality inspection personnel, quality inspection personnel visually observes the outer of product and takes into consideration
Piece empirically provides judgement in turn；Second method is the semi-automatic quality inspection mode of machine auxiliary, mainly centainly judges energy by having
The quality inspection system of power, which filters out, does not have defective photo, is then detected again by photo of the quality inspection personnel to doubtful existing defects
Judgement.Therefore, compared to the prior art, the quality determining method for the plastic foam cutlery box that the embodiment of the present invention proposes, not only may be used
To reduce the detection efficiency of plastic foam cutlery box, but also the detection accuracy of plastic foam cutlery box can be improved；Also, this hair
The technical solution realization of bright embodiment is simple and convenient, it is universal to be convenient for, and the scope of application is wider.
Embodiment two
Fig. 2 is the flow diagram of the quality determining method of plastic foam cutlery box provided by Embodiment 2 of the present invention.Such as Fig. 2
Shown, the quality determining method of plastic foam cutlery box may comprise steps of:
S201, the image data for obtaining plastic foam cutlery box to be detected.
It in a specific embodiment of the present invention, can be with when the surface state to plastic foam cutlery box carries out quality testing
The image data of plastic foam cutlery box to be detected is obtained by the equipment such as video camera.
S202, the quality testing that the image data of foam cutlery box to be detected is converted to plastic foam cutlery box to be detected
Request.
In a specific embodiment of the present invention, when the surface state to plastic foam cutlery box carries out quality testing, pass through
It, can be by foam cutlery box to be detected after the equipment such as video camera gets the image data of plastic foam cutlery box to be detected
Image data be converted to plastic foam cutlery box to be detected quality testing request.It specifically, can be according to predetermined
Format transformation requests the quality testing that the image data of foam cutlery box to be detected is converted to plastic foam cutlery box to be detected.
For example, the quality testing request of plastic foam cutlery box to be detected may include: quality testing order and foam to be detected meal
The image data of box.
S203, acquisition is deployed with the classification and Detection model based on picture classification in pre-set server configuration table
The load condition of each server.
It in a specific embodiment of the present invention, can be first to the deployment of detection model before sending quality testing request
Situation is monitored, and the deployment scenario of detection model in each server is recorded in pre-set server configuration table,
Therefore, in this step, it can be obtained in pre-set server configuration table and be deployed with the classification inspection based on picture classification
Survey the load condition of each server of model.
S204, basis are deployed with the load condition of each server of classification and Detection model, and quality testing is requested to send
To the smallest server of load for being deployed with the classification and Detection model based on picture classification.
In a specific embodiment of the present invention, it gets and is deployed with based on picture in pre-set server configuration table
After the load condition of each server of the classification and Detection model of classification, according to each service for being deployed with classification and Detection model
The load condition of device, by quality testing request be sent to be deployed with the model of the classification and Detection based on picture classification load it is the smallest
Server improves system operational speed to realize balance dispatching.
S205, the quality testing classification request pair that server is exported by the classification and Detection model based on picture classification is received
The quality measurements answered.
In a specific embodiment of the present invention, the server for being deployed with the classification and Detection model based on picture classification can lead to
The classification and Detection model based on picture classification after training in advance calculates the corresponding quality measurements of quality testing request.Cause
This can receive server and classified by the quality testing that the classification and Detection model based on picture classification exports in this step
Request corresponding quality measurements.
Preferably, in a specific embodiment of the present invention, it can also be according to pre-set quality measurements and defect
The corresponding relationship of processing operation makes the corresponding defect processing operation of quality measurements；Wherein, defect processing, which operates, includes:
Alarm shuts down, stores log or control mechanical arm.
The quality determining method for the plastic foam cutlery box that the embodiment of the present invention proposes first obtains plastic foam meal to be detected
The image data of box；Then the image data of foam cutlery box to be detected is converted to the quality of plastic foam cutlery box to be detected
Detection request；Quality testing request is sent to the server for being deployed with the model of the classification and Detection based on picture classification again；The clothes
Business device calculates quality testing by the classification and Detection model based on picture classification of training in advance and requests corresponding quality testing knot
Fruit.That is, in the inventive solutions, the classification and Detection mould based on picture classification that server passes through training in advance
Type carries out quality testing to plastic foam cutlery box to be detected.And in the quality determining method of existing two kinds of plastic foam cutlery boxs
In, first method is pure artificial quality inspection mode, and dependent on the experience of quality inspection personnel, quality inspection personnel visually observes the outer of product and takes into consideration
Piece empirically provides judgement in turn；Second method is the semi-automatic quality inspection mode of machine auxiliary, mainly centainly judges energy by having
The quality inspection system of power, which filters out, does not have defective photo, is then detected again by photo of the quality inspection personnel to doubtful existing defects
Judgement.Therefore, compared to the prior art, the quality determining method for the plastic foam cutlery box that the embodiment of the present invention proposes, not only may be used
To reduce the detection efficiency of plastic foam cutlery box, but also the detection accuracy of plastic foam cutlery box can be improved；Also, this hair
The technical solution realization of bright embodiment is simple and convenient, it is universal to be convenient for, and the scope of application is wider.
Embodiment three
Fig. 3 is the flow diagram of the quality determining method for the plastic foam cutlery box that the embodiment of the present invention three provides.Such as Fig. 3
Shown, the quality determining method of plastic foam cutlery box may comprise steps of:
S301, the quality testing request for receiving plastic foam cutlery box to be detected.
In a specific embodiment of the present invention, when the surface state to plastic foam cutlery box carries out quality testing, pass through
It, can be by foam cutlery box to be detected after the equipment such as video camera gets the image data of plastic foam cutlery box to be detected
Image data be converted to plastic foam cutlery box to be detected quality testing request.It specifically, can be according to predetermined
Format transformation requests the quality testing that the image data of foam cutlery box to be detected is converted to plastic foam cutlery box to be detected.
For example, the quality testing request of plastic foam cutlery box to be detected may include: quality testing order and foam to be detected meal
The image data of box.Therefore, in this step, it can receive the quality testing request of plastic foam cutlery box to be detected.
S302, the corresponding matter of quality testing request is calculated by the classification and Detection model based on picture classification of training in advance
Measure testing result.
In a specific embodiment of the present invention, the server for being deployed with the classification and Detection model based on picture classification can lead to
The classification and Detection model based on picture classification after training in advance calculates the corresponding quality measurements of quality testing request.
Fig. 4 is the schematic illustration for the classification and Detection model based on picture classification that the embodiment of the present invention three provides.Such as Fig. 4
Shown, the classification and Detection model based on picture classification includes: depth convolutional neural networks and defect location sorter network；Wherein,
Depth convolutional neural networks are used to extract the feature of steel plate picture, and feature is input in defect location sorter network；Defect
Positioning sorter network judges that the extracted feature of depth convolutional neural networks is for the classification and Detection algorithm based on picture classification
No existing defects, and obtain the classification of the defect.
Picture classification (image classification), gives an input picture, and image classification task is intended to judge
The image generic.Convolutional layer usually is represented with conv, bn represents batch normalizing layer, pool representative converges layer.It is most common
Network structure sequence is: conv- > bn- > relu- > pool.For example, a picture can be extracted from some photographic model,
And a possibility that giving his four kinds of labels: cat, dog, cap and cup.The picture is identified as large-scale three in a computer
Dimension group.Such as.Cat has 248 pixels wide, and 400 pixels are high, and there are three types of color (red, green, blues).Therefore, which includes
248 × 400 × 3 numbers, altogether 297600 numbers.Each number is an integer data from 0 to 255.Ours appoints
Business is exactly this data up to a million or a label, such as " cat ".
Fig. 5 is the schematic illustration for the depth convolutional neural networks that the embodiment of the present invention three provides.As shown in figure 5, depth
Convolutional neural networks include: convolutional layer, pond layer and full articulamentum；Wherein, convolutional layer, for the convolution kernel different using weight
Convolution is scanned to the image of the plastic foam cutlery box to be detected in quality testing request, therefrom extracts the spy of various meanings
Sign, and export into characteristic pattern；Pond layer, for carrying out dimensionality reduction operation to characteristic pattern；Full articulamentum, the spy for will extract
Sign is mapped in defect location sorter network.It is this to utilize the deep neural network model that there is convolution, pondization to operate, it can be right
The robustness with higher such as the deformation of image on production line, fuzzy, illumination variation, for classification task have it is higher can be general
The property changed.Full articulamentum is then by the Feature Mapping of extraction to defect location sorter network.For different production scene and data
Feature, the depth nerve that can be made up of design different depth, different number neuron, unused convolution pond organizational form
Network model, then using the historical data that has marked, using signal propagated forward, the mode of error back propagation to model into
Row training.When the error amount between the output of model and label is less than the preset threshold value for meeting business need, stop
Training.
Fig. 6 is the structural schematic diagram for the detection system that the embodiment of the present invention three provides.As shown in fig. 6, in implementing utilization
The image data of plastic foam cutlery box to be detected can be acquired using camera, and be sent to console and production number
According to library；After console receives the image data of plastic foam cutlery box to be detected, according to being deployed with based on picture classification
Quality testing request is sent to point being deployed with based on picture classification by the load condition of each server of classification and Detection model
On the smallest server of the load of class detection model.By being deployed with based on the server of the classification and Detection model of picture classification
After calculation, quality measurements are exported, and be sent to controller.Controller controls alarm and is reported according to quality measurements
It is alert, while quality testing log being stored into Production database.Then, it then for detection model is updated operation, it can be with
By updating the data in Production database into tranining database, the embodiment of the present invention then being executed by training engine and is provided
Method detection model is trained, update detection model.
The quality determining method for the plastic foam cutlery box that the embodiment of the present invention proposes first obtains plastic foam meal to be detected
The image data of box；Then the image data of foam cutlery box to be detected is converted to the quality of plastic foam cutlery box to be detected
Detection request；Quality testing request is sent to the server for being deployed with the model of the classification and Detection based on picture classification again；The clothes
Business device calculates quality testing by the classification and Detection model based on picture classification of training in advance and requests corresponding quality testing knot
Fruit.That is, in the inventive solutions, the classification and Detection mould based on picture classification that server passes through training in advance
Type carries out quality testing to plastic foam cutlery box to be detected.And in the quality determining method of existing two kinds of plastic foam cutlery boxs
In, first method is pure artificial quality inspection mode, and dependent on the experience of quality inspection personnel, quality inspection personnel visually observes the outer of product and takes into consideration
Piece empirically provides judgement in turn；Second method is the semi-automatic quality inspection mode of machine auxiliary, mainly centainly judges energy by having
The quality inspection system of power, which filters out, does not have defective photo, is then detected again by photo of the quality inspection personnel to doubtful existing defects
Judgement.Therefore, compared to the prior art, the quality determining method for the plastic foam cutlery box that the embodiment of the present invention proposes, not only may be used
To reduce the detection efficiency of plastic foam cutlery box, but also the detection accuracy of plastic foam cutlery box can be improved；Also, this hair
The technical solution realization of bright embodiment is simple and convenient, it is universal to be convenient for, and the scope of application is wider.
Example IV
Fig. 7 is the structural schematic diagram of the quality detection device for the plastic foam cutlery box that the embodiment of the present invention four provides.Such as Fig. 7
Shown, the quality detection device of plastic foam cutlery box described in the embodiment of the present invention may include: image collection module 701, figure
As conversion module 702, request sending module 703 and result receiving module 704；Wherein,
Described image obtains module 701, for obtaining the image data of plastic foam cutlery box to be detected；
Described image conversion module 702, for by the image data of the foam cutlery box to be detected be converted to it is described to
The quality testing of the plastic foam cutlery box of detection is requested；
The request sending module 703 is deployed with for quality testing request to be sent to based on picture classification
The server of classification and Detection model；
The result receiving module 704 passes through the classification and Detection based on picture classification for receiving the server
Corresponding quality measurements are requested in the quality testing classification of model output.
Further, the request sending module 703 is specifically used for the acquisition unit in pre-set server configuration table
There is the load condition of each server of the classification and Detection model based on picture classification in administration；It is examined according to the classification is deployed with
Quality testing request is sent to described in being deployed with based on picture classification by the load condition for surveying each server of model
The smallest server of the load of classification and Detection model.
Further, described device further include: 705 (not shown) of operation processing module is preset for basis
Quality measurements and defect processing operation corresponding relationship, make the quality measurements corresponding defect processing behaviour
Make；Wherein, the defect processing operation includes: alarm, shutdown, storage log or control mechanical arm.
Method provided by any embodiment of the invention can be performed in the quality detection device of above-mentioned plastic foam cutlery box, has
The corresponding functional module of execution method and beneficial effect.The not technical detail of detailed description in the present embodiment, reference can be made to this hair
The quality determining method for the plastic foam cutlery box that bright any embodiment provides.
Embodiment five
Fig. 8 is the structural schematic diagram of the quality detection device for the plastic foam cutlery box that the embodiment of the present invention five provides.Such as Fig. 8
Shown, the quality detection device of plastic foam cutlery box described in the embodiment of the present invention may include: request receiving module 801 and knot
Fruit computing module 802；Wherein,
The request receiving module 801, the quality testing for receiving plastic foam cutlery box to be detected are requested；
The result computing module 802, for being calculated by the classification and Detection model based on picture classification of training in advance
Corresponding quality measurements are requested in the quality testing.
Further, the classification and Detection model based on picture classification includes: that depth convolutional neural networks and defect are fixed
Position sorter network；Wherein, the depth convolutional neural networks are used to extract the feature of steel plate picture, and the feature is input to
In the defect location sorter network；The defect location sorter network is for the classification and Detection algorithm judgement based on picture classification
The extracted feature of depth convolutional neural networks whether existing defects, and obtain the classification of the defect.
Further, the depth convolutional neural networks include: convolutional layer, pond layer and full articulamentum；Wherein, the volume
Lamination, the convolution kernel for utilizing weight different is to the plastic foam cutlery box to be detected in quality testing request
Image is scanned convolution, therefrom extracts the feature of various meanings, and export into characteristic pattern；The pond layer, for spy
Sign figure carries out dimensionality reduction operation；The full articulamentum, for by the Feature Mapping extracted into the defect location sorter network.
Method provided by any embodiment of the invention can be performed in the quality detection device of above-mentioned plastic foam cutlery box, has
The corresponding functional module of execution method and beneficial effect.The not technical detail of detailed description in the present embodiment, reference can be made to this hair
The quality determining method for the plastic foam cutlery box that bright any embodiment provides.
Embodiment six
Fig. 9 is the structural schematic diagram for the server that the embodiment of the present invention six provides.Fig. 9, which is shown, to be suitable for being used to realizing this hair
The block diagram of the exemplary servers of bright embodiment.The server 12 that Fig. 9 is shown is only an example, should not be to of the invention real
The function and use scope for applying example bring any restrictions.
As shown in figure 9, server 12 is showed in the form of universal computing device.The component of server 12 may include but not
Be limited to: one or more processor or processing unit 16, system storage 28 connect different system components (including system
Memory 28 and processing unit 16) bus 18.
Bus 18 indicates one of a few class bus structures or a variety of, including memory bus or Memory Controller,
Peripheral bus, graphics acceleration port, processor or the local bus using any bus structures in a variety of bus structures.It lifts
For example, these architectures include but is not limited to industry standard architecture (ISA) bus, microchannel architecture (MAC)
Bus, enhanced isa bus, Video Electronics Standards Association (VESA) local bus and peripheral component interconnection (PCI) bus.
Server 12 typically comprises a variety of computer system readable media.These media can be and any can be serviced
The usable medium that device 12 accesses, including volatile and non-volatile media, moveable and immovable medium.
System storage 28 may include the computer system readable media of form of volatile memory, such as arbitrary access
Memory (RAM) 30 and/or cache memory 32.Server 12 may further include other removable/nonremovable
, volatile/non-volatile computer system storage medium.Only as an example, storage system 34 can be used for reading and writing not removable
Dynamic, non-volatile magnetic media (Fig. 9 do not show, commonly referred to as " hard disk drive ").Although being not shown in Fig. 9, can provide
Disc driver for being read and write to removable non-volatile magnetic disk (such as " floppy disk "), and to removable anonvolatile optical disk
The CD drive of (such as CD-ROM, DVD-ROM or other optical mediums) read-write.In these cases, each driver can
To be connected by one or more data media interfaces with bus 18.Memory 28 may include at least one program product,
The program product has one group of (for example, at least one) program module, these program modules are configured to perform each implementation of the invention
The function of example.
Program/utility 40 with one group of (at least one) program module 42 can store in such as memory 28
In, such program module 42 include but is not limited to operating system, one or more application program, other program modules and
It may include the realization of network environment in program data, each of these examples or certain combination.Program module 42 is usual
Execute the function and/or method in embodiment described in the invention.
Server 12 can also be logical with one or more external equipments 14 (such as keyboard, sensing equipment, display 24 etc.)
Letter, can also be enabled a user to one or more equipment interact with the server 12 communicate, and/or with make the server
The 12 any equipment (such as network interface card, modem etc.) that can be communicated with one or more of the other calculating equipment communicate.
This communication can be carried out by input/output (I/O) interface 22.Also, server 12 can also pass through network adapter 20
With one or more network (such as local area network (LAN), wide area network (WAN) and/or public network, such as internet) communication.
As shown, network adapter 20 is communicated by bus 18 with other modules of server 12.It should be understood that although not showing in figure
Out, can in conjunction with server 12 use other hardware and/or software module, including but not limited to: microcode, device driver,
Redundant processing unit, external disk drive array, RAID system, tape drive and data backup storage system etc..
Processing unit 16 by the program that is stored in system storage 28 of operation, thereby executing various function application and
Data processing, such as realize the quality determining method of plastic foam cutlery box provided by the embodiment of the present invention.
Embodiment seven
The embodiment of the present invention seven provides a kind of computer storage medium.
The computer readable storage medium of the embodiment of the present invention, can be using one or more computer-readable media
Any combination.Computer-readable medium can be computer-readable signal media or computer readable storage medium.Computer
Readable storage medium storing program for executing for example may be-but not limited to-the system of electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, dress
It sets or device, or any above combination.The more specific example (non exhaustive list) of computer readable storage medium wraps
It includes: there is the electrical connection of one or more conducting wires, portable computer diskette, hard disk, random access memory (RAM), read-only
Memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory
(CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.In this document, computer-readable
Storage medium can be it is any include or storage program tangible medium, the program can be commanded execution system, device or
Device use or in connection.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including but unlimited
In electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be that computer can
Any computer-readable medium other than storage medium is read, which can send, propagates or transmit and be used for
By the use of instruction execution system, device or device or program in connection.
The program code for including on computer-readable medium can transmit with any suitable medium, including --- but it is unlimited
In wireless, electric wire, optical cable, RF etc. or above-mentioned any appropriate combination.
The computer for executing operation of the present invention can be write with one or more programming languages or combinations thereof
Program code, described program design language include object oriented program language-such as Java, Smalltalk, C++,
It further include conventional procedural programming language-such as " C " language or similar programming language.Program code can be with
It fully executes, partly execute on the user computer on the user computer, being executed as an independent software package, portion
Divide and partially executes or executed on a remote computer or server completely on the remote computer on the user computer.?
Be related in the situation of remote computer, remote computer can pass through the network of any kind --- including local area network (LAN) or
Wide area network (WAN)-be connected to subscriber computer, or, it may be connected to outer computer (such as mentioned using Internet service
It is connected for quotient by internet).
Note that the above is only a better embodiment of the present invention and the applied technical principle.It will be appreciated by those skilled in the art that
The invention is not limited to the specific embodiments described herein, be able to carry out for a person skilled in the art it is various it is apparent variation,
It readjusts and substitutes without departing from protection scope of the present invention.Therefore, although being carried out by above embodiments to the present invention
It is described in further detail, but the present invention is not limited to the above embodiments only, without departing from the inventive concept, also
It may include more other equivalent embodiments, and the scope of the invention is determined by the scope of the appended claims.
Claims (14)
1. a kind of quality determining method of plastic foam cutlery box, which is characterized in that the described method includes:
Obtain the image data of plastic foam cutlery box to be detected；
The image data of the foam cutlery box to be detected is converted to the quality testing of the plastic foam cutlery box to be detected
Request；
Quality testing request is sent to the server for being deployed with the model of the classification and Detection based on picture classification；
The server is received to ask by the quality testing classification of the classification and Detection model output based on picture classification
Seek corresponding quality measurements.
2. the method according to claim 1, wherein described be sent to quality testing request is deployed with base
In the server of the classification and Detection model of picture classification, comprising:
Deployment is obtained in pre-set server configuration table is stated each of the classification and Detection model based on picture classification
The load condition of server；
According to the load condition for each server for being deployed with the classification and Detection model, quality testing request is sent to
It is deployed with the smallest server of load of the classification and Detection model based on picture classification.
3. the method according to claim 1, wherein the method also includes:
According to the corresponding relationship of pre-set quality measurements and defect processing operation, the quality measurements pair are made
The defect processing operation answered；Wherein, the defect processing operation includes: alarm, shutdown, storage log or control mechanical arm.
4. a kind of quality determining method of plastic foam cutlery box, which is characterized in that the described method includes:
Receive the quality testing request of plastic foam cutlery box to be detected；
The quality testing, which is calculated, by the classification and Detection model based on picture classification of training in advance requests corresponding quality inspection
Survey result.
5. according to the method described in claim 4, it is characterized in that, the classification and Detection model based on picture classification includes:
Depth convolutional neural networks and defect location sorter network；Wherein, the depth convolutional neural networks are for extracting steel plate picture
Feature, and the feature is input in the defect location sorter network；The defect location sorter network is for being based on
The classification and Detection algorithm of picture classification judge the extracted feature of depth convolutional neural networks whether existing defects, and obtain this lack
Sunken classification.
6. according to the method described in claim 5, it is characterized in that, the depth convolutional neural networks include: convolutional layer, Chi Hua
Layer and full articulamentum；Wherein, the convolutional layer, the convolution kernel for utilizing weight different is to the institute in quality testing request
The image for stating plastic foam cutlery box to be detected is scanned convolution, therefrom extracts the feature of various meanings, and export to feature
In figure；The pond layer, for carrying out dimensionality reduction operation to characteristic pattern；The full articulamentum, the Feature Mapping for will extract
Into the defect location sorter network.
7. a kind of quality detection device of plastic foam cutlery box, which is characterized in that described device includes: image collection module, figure
As conversion module, request sending module and result receiving module；Wherein,
Described image obtains module, for obtaining the image data of plastic foam cutlery box to be detected；
Described image conversion module, for the image data of the foam cutlery box to be detected to be converted to the modeling to be detected
The quality testing of strand foam cutlery box is requested；
The request sending module is deployed with the classification and Detection based on picture classification for quality testing request to be sent to
The server of model；
The result receiving module passes through the classification and Detection model output based on picture classification for receiving the server
Quality testing classification request corresponding quality measurements.
8. device according to claim 7, it is characterised in that:
The request sending module is stated specifically for obtaining deployment in pre-set server configuration table based on picture
The load condition of each server of the classification and Detection model of classification；According to each service for being deployed with the classification and Detection model
Quality testing request is sent to and is deployed with the negative of the classification and Detection model based on picture classification by the load condition of device
Carry the smallest server.
9. device according to claim 7, which is characterized in that described device further include: operation processing module is used for basis
The corresponding relationship of pre-set quality measurements and defect processing operation, makes the corresponding defect of the quality measurements
Processing operation；Wherein, the defect processing operation includes: alarm, shutdown, storage log or control mechanical arm.
10. a kind of quality detection device of plastic foam cutlery box, which is characterized in that described device include: request receiving module and
As a result computing module；Wherein,
The request receiving module, the quality testing for receiving plastic foam cutlery box to be detected are requested；
The result computing module, for calculating the quality by the classification and Detection model based on picture classification of training in advance
Corresponding quality measurements are requested in detection.
11. device according to claim 10, which is characterized in that the classification and Detection model packet based on picture classification
It includes: depth convolutional neural networks and defect location sorter network；Wherein, the depth convolutional neural networks are for extracting steel plate figure
The feature of piece, and the feature is input in the defect location sorter network；The defect location sorter network is used for base
In the classification and Detection algorithm of picture classification judge the extracted feature of depth convolutional neural networks whether existing defects, and be somebody's turn to do
The classification of defect.
12. device according to claim 11, which is characterized in that the depth convolutional neural networks include: convolutional layer, pond
Change layer and full articulamentum；Wherein, the convolutional layer, the convolution kernel for utilizing weight different is in quality testing request
The image of the plastic foam cutlery box to be detected is scanned convolution, therefrom extracts the feature of various meanings, and export to spy
It levies in figure；The pond layer, for carrying out dimensionality reduction operation to characteristic pattern；The full articulamentum, for reflecting the feature extracted
It is mapped in the defect location sorter network.
13. a kind of server characterized by comprising
One or more processors；
Memory, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processors are real
The now quality determining method of the plastic foam cutlery box as described in any one of claims 1 to 3 or 4 to 6.
14. a kind of storage medium, is stored thereon with computer program, which is characterized in that the realization when program is executed by processor
The quality determining method of plastic foam cutlery box as described in any one of claims 1 to 3 or 4 to 6.
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