CN110009614A - Method and apparatus for output information - Google Patents
Method and apparatus for output information Download PDFInfo
- Publication number
- CN110009614A CN110009614A CN201910248452.4A CN201910248452A CN110009614A CN 110009614 A CN110009614 A CN 110009614A CN 201910248452 A CN201910248452 A CN 201910248452A CN 110009614 A CN110009614 A CN 110009614A
- Authority
- CN
- China
- Prior art keywords
- image
- detection model
- defects detection
- defect
- classification
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- 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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- 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
- 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
- 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
- G06T2207/10004—Still image; Photographic 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
- G06T2207/20081—Training; Learning
-
- 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
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
-
- 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
Abstract
Embodiment of the disclosure discloses the method and apparatus for output information.One specific embodiment of this method includes: the image for obtaining object to be detected；By image input defects detection model trained in advance, the corresponding defect classification of image and position are obtained, wherein defects detection model uses Faster RCNN algorithm；Export the corresponding defect classification of image and position.This embodiment improves the efficiency and accuracy of object appearance quality testing.
Description
Technical field
This disclosure relates to field of computer technology, and in particular to the method and apparatus for output information.
Background technique
In existing industrial production line quality inspection system, the detection and positioning of defect picture rely primarily on pure manual inspection or are based on
The detection system of Feature Engineering.In the case where artificial quality inspection, business expert is needed to carry out walkaround inspection, discovery in production scene
Manual record gets off after defect does subsequent processing again.This method not only low efficiency, is easy erroneous judgement of failing to judge, data are difficult to carry out
Secondary use is excavated, and industrial production environment is often relatively more severe, will cause adverse effect to the health and safety of personnel.?
In quality inspection system based on traditional expert system or Feature Engineering, feature and decision rule are all based on experience and are cured in machine
, it is difficult to the development iteration of business, lead to the development with production technology, the detection accuracy of system is lower and lower, or even drop
As low as complete unusable state.In addition, the feature of traditional quality inspection system is all solidified by third-party vendor within hardware in advance,
It not only needs to carry out key technological transformation to production line when upgrading, but also expensive.Traditional quality inspection system safety, standardization,
All there is obvious deficiencies for scalability etc., are unfavorable for the optimization and upgrading of traditional industry production line.
Summary of the invention
Embodiment of the disclosure proposes the method and apparatus for output information.
In a first aspect, embodiment of the disclosure provides a kind of method for output information, comprising: obtain to be detected
The image of object；By image input defects detection model trained in advance, the corresponding defect classification of image and position are obtained,
In, defects detection model uses Faster RCNN algorithm；Export the corresponding defect classification of image and position.
In some embodiments, obtain the image of object to be detected, comprising: according at least one of following parameter obtain to
At least one image of the object of detection: angle, filter, times mirror, focuses light.
In some embodiments, object be wooden objects and defect classification include at least one of the following: worm hole, cracking,
Collapse scarce, incrustation.
In some embodiments, defects detection model is using SE-ResNet as basic network.
In some embodiments, training obtains defects detection model as follows: obtaining training sample set, training
Sample includes the sample image, defect type corresponding with sample image and position of object to be detected；By training sample set
In training sample sample image as input, using defect type corresponding with the sample image of input and position as defeated
Out, training obtains defects detection model.
In some embodiments, defects detection model, comprising: feature extraction layer, the pond ROI layer, divides region recommendation network
Class returns layer.
In some embodiments, the penalty values of defects detection model include region recommendation network penalty values, Classification Loss value,
Return penalty values.
Second aspect, embodiment of the disclosure provide a kind of device for output information, comprising: acquiring unit, quilt
It is configured to obtain the image of object to be detected；Detection unit is configured to inputting image into defects detection mould trained in advance
Type obtains the corresponding defect classification of image and position, wherein defects detection model uses Faster RCNN algorithm；Output is single
Member is configured to export the corresponding defect classification of image and position.
In some embodiments, acquiring unit is further configured to: being obtained according at least one of following parameter to be detected
Object at least one image: angle, light, filter, times mirror, focus.
In some embodiments, object be wooden objects and defect classification include at least one of the following: worm hole, cracking,
Collapse scarce, incrustation.
In some embodiments, defects detection model is using SE-ResNet as basic network.
In some embodiments, which further includes training unit, is configured to: obtaining training sample set, training sample
This includes sample image, defect type corresponding with sample image and the position of object to be detected；It will be in training sample set
Training sample sample image as input, using defect type corresponding with the sample image of input and position as exporting,
Training obtains defects detection model.
In some embodiments, defects detection model, comprising: feature extraction layer, the pond ROI layer, divides region recommendation network
Class returns layer.
In some embodiments, the penalty values of defects detection model include region recommendation network penalty values, Classification Loss value,
Return penalty values.
The third aspect, embodiment of the disclosure provide a kind of electronic equipment, comprising: one or more processors；Storage
Device is stored thereon with one or more programs, when one or more programs are executed by one or more processors, so that one
Or multiple processors are realized such as method any in first aspect.
Fourth aspect, embodiment of the disclosure provide a kind of computer-readable medium, are stored thereon with computer program,
Wherein, it realizes when program is executed by processor such as method any in first aspect.
The method and apparatus for output information that embodiment of the disclosure provides, by being produced using image capture device
The image acquired in real time on product production line carries out detection judgement to the surface quality of product in real time, if detecting current process
There are quality problems for the product of image capture device, then judge the type of the quality problems and the position gone wrong.Improve object
The efficiency and accuracy of external table quality testing.And convenient for statistical shortcomings information to facilitate subsequent quality management &control.
Detailed description of the invention
By reading a detailed description of non-restrictive embodiments in the light of the attached drawings below, the disclosure is other
Feature, objects and advantages will become more apparent upon:
Fig. 1 is that the embodiment of the present application can be applied to exemplary system architecture figure therein；
Fig. 2 is the flow chart according to one embodiment of the method for output information of the application；
Fig. 3 a, 3b, 3c are the network knots of the defects detection model used according to the method for output information of the application
Composition；
Fig. 4 is the flow chart according to one embodiment of the defects detection model training method of the application；
Fig. 5 is the structural schematic diagram according to one embodiment of the device for output information of the application；
Fig. 6 is adapted for the structural schematic diagram for the computer system for realizing the electronic equipment of the embodiment of the present application.
Specific embodiment
The disclosure 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 related invention, rather than the restriction to the invention.It also should be noted that in order to
Convenient for description, part relevant to related invention is illustrated only in attached drawing.
It should be noted that in the absence of conflict, the feature in embodiment and embodiment in the disclosure can phase
Mutually combination.The disclosure is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
Fig. 1 is shown can be using the method for output information of the disclosure or the implementation of the device for output information
The exemplary system architecture 100 of example.
As shown in Figure 1, system architecture 100 may include object 101 to be detected, camera 1021,1022,1023,
1024, server 103.Network is to provide communication link between 1024, server 103 in camera 1021,1022,1023
Medium.Network may include various connection types, such as wired, wireless communication link or fiber optic cables etc..
Object 101 to be detected can be the product for needing to carry out appearance detection on production line.Especially wooden product,
For example, wooden toy etc..The appearance of these toys be limited to using timber, it is possible that it is some as caused by timber lack
Fall into, such as worm hole, cracking, collapse it is scarce, the defects of incrustation, it is therefore desirable to carry out surface quality detection to wooden toy.
Server 103 can include: image capturing system, console, defects detection model, training engine, control module, instruction
Practice each and every one several main modulars such as database, Production database, service response system.
Image capturing system, using high precision image acquisition camera, adjustment angle, filter, times mirror, is focused light, is adopted
Collect multiple images for subsequent processing.
The picture that image capturing system on production line generates in real time is converted detection request by console, and according to pre- on line
The deployment scenario real-time perfoming load balancing and scheduling for surveying model will test request and be sent to the optimal prediction model that carries
On server.Defects detection model when running really on the server, the model are completed via training engine training.Model pair
After image data in the detection request of arrival carries out preset pretreatment, object detection calculating is carried out, and provides representative and lacks
The location information of sunken classification information and defect, and result is sent to control module.Control module is set in conjunction with business scenario
Meter, can make the response for meeting production environment scene requirement to the prediction result that model provides according to business demand, such as report
Alert, storage log, control mechanical arm etc..Control module can be using the processing behavior of prediction result and response as producing log on line
It stores in Production database.The core of system-defects detection model is according to history labeled data trained by training engine
It arrives.
Defects detection model uses depth convolutional neural networks structure, is examined using the object detection algorithms in computer vision
Survey defective locations.The disclosure uses the FasterRCNN in object detection algorithms (quickly based on the convolutional neural networks in region)
Algorithm.Input of the original image as model on production line, defect classification and position are as output.The structure of network is mainly
By convolutional layer, pond layer, connects layer entirely and connect etc. and form.Wherein convolution operation is using the different convolution kernel of weight to original image or spy
Sign figure (feature map) is scanned convolution, therefrom extracts the feature of various meanings, and export into characteristic pattern.Chi Huacao
Make then to carry out dimensionality reduction operation to characteristic pattern, the main feature in keeping characteristics figure.Utilize this depth operated with convolution, pondization
Spend neural network model, can to robustness with higher such as the deformation of photo on production line, fuzzy, illumination variations, for
Classification task have it is higher can generalization.
Trained model each time can gradually be replaced by the online mode of small flow just running on line it is old
Model is extended with to achieve the purpose that model with service dynamic extensive.
It should be noted that the method provided by embodiment of the disclosure for output information is generally by server 103
It executes, correspondingly, the device for output information is generally positioned in server 103.
It should be understood that object to be detected, camera in Fig. 1, the number of server are only schematical.According to reality
It now needs, can have any number of object to be detected, camera, server.
With continued reference to Fig. 2, the process of one embodiment of the method for output information according to the disclosure is shown
200.This is used for the method for output information, comprising the following steps:
Step 201, the image of object to be detected is obtained.
In the present embodiment, can lead to for the executing subject of the method for output information (such as server shown in FIG. 1)
It crosses wired connection mode or radio connection and obtains image from the camera of shooting examined object.Camera can be acquired
Image pre-processed, remove background image after obtain the image of object to be detected.For example, being examined by edge detection algorithm
The position of object is measured to pluck out the image of object to be detected from the image that camera is shot.Controllable camera adjustment
Angle, or the image by the different angle of the camera acquisition object of different location.Also rotatable examined object is to obtain
Take the image of multi-angle.Optionally, also object to be detected can be obtained by adjusting at least one following parameter of camera
At least one image: angle, filter, times mirror, focuses light.For example, flash lamp can be obtained open and do not open the image of flash lamp.
Step 202, the defects detection model that image input is trained in advance, obtains the corresponding defect classification of image and position
It sets.
In the present embodiment, which can judge to whether there is defect and defect according to the image of input
Classification and position.If object be it is wooden, defect classification may include at least one of following: worm hole cracks, collapses scarce, knot
Scab.Not having defect is also one of defect classification.Faster RCNN algorithm can be used in defects detection model.
As shown in Figure 3a, Faster RCNN algorithm extracts characteristic pattern by feature extraction layer first, such as utilizes classification mould
Type obtains its characteristic pattern using the convolution operation of basic network as basic network.Then RPN (Region Proposal is utilized
Network, candidate region network) it whether calculates in a certain region of original image comprising specific object: if sharp again comprising object
Feature extraction is carried out with ROI (region of interest) pond layer, layer is then returned by classification and predicts its object category
With boundary box (bounding box)；Without classification if not including object.In this way, by the loss of three network branches
It combines, does combined training, Optimized model parameter.When the error amount between the output and true value of model is less than certain threshold
When value, deconditioning.
The basic network of feature extraction layer can be the networks such as AlexNet, VGG, GoogleNet, ResNet.
RPN network is mainly used for generating region proposals (region recommendation), firstly generates a pile Anchor box
(anchor point box), it is carried out after cutting filtering by softmax judge Anchors belong to prospect (foreground) or after
Scape (background), i.e. " being object " or " not being object ", so this is one two classification.Meanwhile another branch
Bounding box regression (recurrence of boundary box) corrects anchor box, forms more accurate region
proposals。
In the region proposals and feature extraction layer that ROI Pooling (pond ROI layer) layer utilizes RPN to generate most
The feature map (characteristic pattern) that later layer obtains obtains the proposal feature map (recommended characteristics of fixed size
Figure), target identification and positioning can be carried out using full attended operation below by entering.
Classification, which returns layer, can form the pond ROI layer the full attended operation of feature map progress of fixed size, utilize
Softmax carries out the classification of specific category, meanwhile, bounding box regression, which is completed, using L1Loss returns operation
Obtain the exact position of object.
In some optional implementations of the present embodiment, SE- is can be used in the basic network for extracting characteristic pattern
ResNet (squeezes excitation residual error network).On the basis of ResNet, extruding (Squeeze) and excitation are increased
(Excitation) it operates, can make full use of relationship between characteristic pattern difference channel.It specifically, is exactly the side for passing through study
Formula gets the significance level in each feature channel automatically, then goes to promote useful feature and press down according to this significance level
Make the feature little to current task use.Fig. 3 b is the schematic diagram for the SE-ResNet that the disclosure uses.An input x is given,
Its feature port number is c1, it is c by obtaining a feature port number after a series of General Transformations such as convolution2Feature.With tradition
CNN (convolutional neural networks) it is different be that next we operate the feature that is previously obtained come recalibration by three.
It is Squeeze operation first, we carry out Feature Compression along Spatial Dimension, by each two-dimensional feature channel
Become a real number, the feature of dimension and input that this real number has global receptive field in a way, and exports is logical
Road number matches.It characterizes the global distribution responded on feature channel, and the layer close to input can also be obtained
Global receptive field, this point is all highly useful in many tasks.
Followed by Excitation operation, it is the mechanism for being similar to door in Recognition with Recurrent Neural Network.By parameter w come
Weight is generated for each feature channel, wherein parameter w is learnt correlation for explicitly Modelling feature interchannel.
Finally the operation of Reweight (again weight), we by the weight of the output of Excitation regard as into
Then the importance in each feature channel after crossing feature selecting is completed by multiplication by channel weighting to previous feature
The recalibration to primitive character on channel dimension.Fig. 3 c is the structure example being embedded into SE-ResNet in ResNet.Side
Dimensional information beside frame represents the output of this layer.Here we use global pooling (pond Quan Pingjun) as
Squeeze operation.And then two FC (Fully Connected, complete to connect) layers form Bottleneck (bottleneck) knot
Structure goes the correlation of modeling interchannel, and exports and the same number of weight of input feature vector.We first reduce characteristic dimension
To the 1/16 of input, then pass through one again after ReLu (Rectified Linear Unit corrects linear unit) activation
Connected layers of Fully rise and return to original dimension.It does so than directly being existed with one Connected layers of Fully of benefit
In: 1) have more non-linear, can preferably be fitted the correlation of interchannel complexity；2) considerably reduce parameter amount and
Calculation amount.Then normalized weight between 0~1 is obtained by the door of a Sigmoid, finally by a Scale (contracting
Put) operation will normalize after Weight to the feature in each channel on.In module basis above, according to
The overall structure of ResNet is overlapped, available SE-ResNet.
Step 203, the corresponding defect classification of output image and position.
In the present embodiment, after step 202, defect classification and the position of at least one defect is can be obtained in each image
It sets.After the corresponding defect classification of each image of same object and position grouping are analyzed, the defect letter of the object can be obtained
Breath.Defect information may include defect classification and position.
Here output can be output in the display being connected with server, can also pass through voice prompting staff
There are defective products.Or defect information directly is sent to the device that mechanical arm etc. is used to screen substandard products.Mechanical arm
Defective object can be picked and, or even different recovery zones can be put into according to defect type.
The method provided by the above embodiment of the disclosure passes through lacking the image input training in advance of object to be detected
Fall into detection model, obtained defect classification and position.Improve the efficiency and accuracy of object appearance quality testing.And defect
Information can carry out secondary use excavation, improve the quality of product and reduce production cost.
With further reference to Fig. 4, it illustrates according to one embodiment of the defects detection model training method of the application
Process 400.The process 400 of the defects detection model training method, comprising the following steps:
Step 401, training sample set is obtained.
In the present embodiment, electronic equipment (such as the clothes shown in FIG. 1 of defects detection model training method operation thereon
Business) available training sample set, wherein training sample includes the sample image of object to be detected and corresponding with sample image
Defect type and position.The defects of training sample type and position can be to be marked by hand.
Step 402, the sample image for each training sample that training sample is concentrated is sequentially input to initialization defect inspection
Model is surveyed, prediction defect type and position corresponding to each sample image are obtained.
In the present embodiment, the sample image based on object to be detected acquired in step 401, electronic equipment can incite somebody to action
The sample image for each training sample that training sample is concentrated is sequentially input to initialization defects detection model, to obtain each
Prediction defect type and position corresponding to sample image.Here, electronic equipment can lack each sample image from initialization
The input side input of detection model is fallen into, successively by the processing of the parameter of each layer in initialization defects detection model, and from first
The outlet side of beginningization defects detection model exports, and the information of outlet side output is prediction defect class corresponding to the sample image
Type and position.Wherein, the defect that initial imperfection detection model can be unbred defects detection model or training is not completed
Detection model, each layer are provided with initiation parameter, and initiation parameter can be by not in the training process of defects detection model
It adjusts disconnectedly.Initialization defects detection model can use VGG, ResNet, SE-ResNet, Faster RCNN even depth convolution
Neural network structure.
Step 403, defect type and position corresponding to the sample image in each training sample training sample concentrated
It sets and is compared with the prediction defect type of the sample image and position, the prediction for obtaining initialization defects detection model is accurate
Rate.
In the present embodiment, the prediction defect type based on the obtained sample image of step 402, electronic equipment can incite somebody to action
The prediction defect type and position of defect type corresponding to sample image and position and sample image are compared in training sample
Compared with to obtain the predictablity rate of initialization defects detection model.Specifically, if sample image institute is right in a training sample
The defect type and position answered are same or similar with the prediction defect type of the sample image and position, then initialize defects detection
Model prediction is correct；If the prediction of defect type and position and the sample image corresponding to sample image in a training sample
Defect type is different with position or not close, then initializes defects detection model prediction mistake.Here, electronic equipment can calculate
Predict the ratio of correct number and total sample number, and the predictablity rate as initialization defects detection model.
In some optional implementations of the present embodiment, the penalty values of defects detection model include region recommendation network
Penalty values, return penalty values at Classification Loss value.Classification Loss value, recurrence penalty values are to respectively correspond softmax, smooth L1
Loss function is calculated, region recommendation network penalty values are classified calculating probability values.
Step 404, determine whether predictablity rate is greater than default accuracy rate threshold value.
In the present embodiment, the predictablity rate based on the obtained initialization defects detection model of step 403, electronics are set
It is standby to be compared the predictablity rate for initializing defects detection model with default accuracy rate threshold value, if more than default accurate
Threshold value is spent, thens follow the steps 405.If more than default accuracy threshold value, 406 are thened follow the steps.
Step 405, the defects detection model completed defects detection model is initialized as training.
In the present embodiment, the case where the prediction accuracy for initializing defects detection model is greater than default accuracy threshold value
Under, illustrate that the defects detection model training is completed, at this point, electronic equipment can will initialization defects detection model as having trained
At defects detection model.
Step 406, the parameter of adjustment initialization defects detection model.
In the present embodiment, the feelings of default accuracy threshold value are not more than in the prediction accuracy of initialization defects detection model
Under condition, the parameter of the adjustable initialization defects detection model of electronic equipment, and 402 are returned to step, until training energy
Enough characterize the defect inspection of the corresponding relationship between the sample image of object to be detected and defect type corresponding with sample image
Until surveying model.
With further reference to Fig. 5, as the realization to method shown in above-mentioned each figure, present disclose provides one kind for exporting letter
One embodiment of the device of breath, the Installation practice is corresponding with embodiment of the method shown in Fig. 2, which can specifically answer
For in various electronic equipments.
As shown in figure 5, the device 500 for output information of the present embodiment includes: acquiring unit 501, detection unit 502
With output unit 503.Wherein, acquiring unit 501 are configured to obtain the image of object to be detected；Detection unit 502, quilt
It is configured to inputting image into defects detection model trained in advance, obtains the corresponding defect classification of image and position, wherein defect
Detection model uses Faster RCNN algorithm；Output unit 503 is configured to export the corresponding defect classification of image and position.
In the present embodiment, for the acquiring unit 501 of the device of output information 500, detection unit 502 and output unit
503 specific processing can be with reference to step 201, the step 202, step 203 in Fig. 2 corresponding embodiment.
In some optional implementations of the present embodiment, acquiring unit 501 is further configured to: according to camera
At least one of following parameter obtain at least one image of object to be detected: angle, filter, times mirror, focuses light.
In some optional implementations of the present embodiment, object is wooden objects and defect classification includes following
At least one of: worm hole cracks, collapses scarce, incrustation.
In some optional implementations of the present embodiment, defects detection model uses net based on SE-ResNet
Network.
In some optional implementations of the present embodiment, device 500 further includes training unit 504, is configured to: obtaining
Take training sample set, training sample include the sample image of object to be detected, defect type corresponding with sample image and
Position；Using the sample image of the training sample in training sample set as input, lacked corresponding with the sample image of input
Type and position are fallen into as output, training obtains defects detection model.
In some optional implementations of the present embodiment, defects detection model, comprising: feature extraction layer, region push away
Recommend network, the pond ROI layer, classification recurrence layer.
In some optional implementations of the present embodiment, the penalty values of defects detection model include region recommendation network
Penalty values, return penalty values at Classification Loss value.
Below with reference to Fig. 6, it illustrates the electronic equipment that is suitable for being used to realize embodiment of the disclosure, (example is as shown in figure 1
Server) 600 structural schematic diagram.Server shown in Fig. 6 is only an example, should not be to the function of embodiment of the disclosure
Any restrictions can be brought with use scope.
As shown in fig. 6, electronic equipment 600 may include processing unit (such as central processing unit, graphics processor etc.)
601, random access can be loaded into according to the program being stored in read-only memory (ROM) 602 or from storage device 608
Program in memory (RAM) 603 and execute various movements appropriate and processing.In RAM 603, it is also stored with electronic equipment
Various programs and data needed for 600 operations.Processing unit 601, ROM 602 and RAM603 are connected with each other by bus 604.
Input/output (I/O) interface 605 is also connected to bus 604.
In general, following device can connect to I/O interface 605: including such as touch screen, touch tablet, keyboard, mouse, taking the photograph
As the input unit 606 of head, microphone, accelerometer, gyroscope etc.；Including such as liquid crystal display (LCD), loudspeaker, vibration
The output device 607 of dynamic device etc.；Storage device 608 including such as tape, hard disk etc.；And communication device 609.Communication device
609, which can permit electronic equipment 600, is wirelessly or non-wirelessly communicated with other equipment to exchange data.Although Fig. 6 shows tool
There is the electronic equipment 600 of various devices, it should be understood that being not required for implementing or having all devices shown.It can be with
Alternatively implement or have more or fewer devices.Each box shown in Fig. 6 can represent a device, can also root
According to needing to represent multiple devices.
Particularly, in accordance with an embodiment of the present disclosure, it may be implemented as computer above with reference to the process of flow chart description
Software program.For example, embodiment of the disclosure includes a kind of computer program product comprising be carried on computer-readable medium
On computer program, which includes the program code for method shown in execution flow chart.In such reality
It applies in example, which can be downloaded and installed from network by communication device 609, or from storage device 608
It is mounted, or is mounted from ROM 602.When the computer program is executed by processing unit 601, the implementation of the disclosure is executed
The above-mentioned function of being limited in the method for example.It should be noted that computer-readable medium described in embodiment of the disclosure can be with
It is computer-readable signal media or computer readable storage medium either the two any combination.It is computer-readable
Storage medium for example may be-but not limited to-the system of electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, device or
Device, or any above combination.The more specific example of computer readable storage medium can include but is not limited to: have
The electrical connection of one or more conducting wires, portable computer diskette, hard disk, random access storage device (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 embodiment of the disclosure, computer
Readable storage medium storing program for executing can be any tangible medium for including or store program, which can be commanded execution system, device
Either device use or in connection.And in embodiment of the disclosure, computer-readable signal media may include
In a base band or as the data-signal that carrier wave a part is propagated, wherein carrying computer-readable program code.It is this
The data-signal of propagation can take various forms, including but not limited to electromagnetic signal, optical signal or above-mentioned any appropriate
Combination.Computer-readable signal media can also be any computer-readable medium other than computer readable storage medium, should
Computer-readable signal media can send, propagate or transmit for by instruction execution system, device or device use or
Person's program in connection.The program code for including on computer-readable medium can transmit with any suitable medium,
Including but not limited to: electric wire, optical cable, RF (radio frequency) etc. or above-mentioned any appropriate combination.
Above-mentioned computer-readable medium can be included in above-mentioned electronic equipment；It is also possible to individualism, and not
It is fitted into the electronic equipment.Above-mentioned computer-readable medium carries one or more program, when said one or more
When a program is executed by the electronic equipment, so that the electronic equipment: obtaining the image of object to be detected；Image input is preparatory
Trained defects detection model obtains the corresponding defect classification of image and position, wherein defects detection model uses Faster
RCNN algorithm；Export the corresponding defect classification of image and position.
The behaviour for executing embodiment of the disclosure can be write with one or more programming languages or combinations thereof
The computer program code of work, described program design language include object oriented program language-such as Java,
Smalltalk, C++ further include conventional procedural programming language-such as " C " language or similar program design language
Speech.Program code can be executed fully on the user computer, partly be executed on the user computer, as an independence
Software package execute, part on the user computer part execute on the remote computer or completely in remote computer or
It is executed on server.In situations involving remote computers, remote computer can pass through the network of any kind --- packet
It includes local area network (LAN) or wide area network (WAN)-is connected to subscriber computer, or, it may be connected to outer computer (such as benefit
It is connected with ISP by internet).
Flow chart and block diagram in attached drawing are illustrated according to the system of the various embodiments of the disclosure, method and computer journey
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part of one module, program segment or code of table, a part of the module, program segment or code include one or more use
The executable instruction of the logic function as defined in realizing.It should also be noted that in some implementations as replacements, being marked in box
The function of note can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are actually
It can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it to infuse
Meaning, the combination of each box in block diagram and or flow chart and the box in block diagram and or flow chart can be with holding
The dedicated hardware based system of functions or operations as defined in row is realized, or can use specialized hardware and computer instruction
Combination realize.
Being described in unit involved in embodiment of the disclosure can be realized by way of software, can also be passed through
The mode of hardware is realized.Described unit also can be set in the processor, for example, can be described as: a kind of processor
Including acquiring unit, detection unit and output unit.Wherein, the title of these units is not constituted under certain conditions to the list
The restriction of member itself, for example, acquiring unit is also described as " obtaining the unit of the image of object to be detected ".
Above description is only the preferred embodiment of the disclosure and the explanation to institute's application technology principle.Those skilled in the art
Member is it should be appreciated that invention scope involved in the disclosure, however it is not limited to technology made of the specific combination of above-mentioned technical characteristic
Scheme, while should also cover in the case where not departing from the inventive concept, it is carried out by above-mentioned technical characteristic or its equivalent feature
Any combination and the other technical solutions formed.Such as features described above has similar function with (but being not limited to) disclosed in the disclosure
Can technical characteristic replaced mutually and the technical solution that is formed.
Claims (16)
1. a kind of method for output information, comprising:
Obtain the image of object to be detected；
By described image input defects detection model trained in advance, the corresponding defect classification of described image and position are obtained,
In, the defects detection model uses Faster RCNN algorithm；
Export the corresponding defect classification of described image and position.
2. according to the method described in claim 1, wherein, the image for obtaining object to be detected, comprising:
At least one image of object to be detected is obtained according at least one of following parameter:
Angle, filter, times mirror, focuses light.
3. according to the method described in claim 1, wherein, the object be wooden objects and the defect classification include with
It is at least one of lower:
Worm hole cracks, collapses scarce, incrustation.
4. according to the method described in claim 1, wherein, the defects detection model is using SE-ResNet as basic network.
5. method described in one of -4 according to claim 1, wherein the defects detection model is trained as follows
It arrives:
Training sample set is obtained, training sample includes the sample image of object to be detected, defect corresponding with sample image
Type and position；
It, will be corresponding with the sample image of input using the sample image of the training sample in the training sample set as input
As output, training obtains the defects detection model for defect type and position.
6. according to the method described in claim 5, wherein, the defects detection model, comprising: feature extraction layer, region are recommended
Network, the pond ROI layer, classification return layer.
7. according to the method described in claim 5, wherein, the penalty values of the defects detection model include region recommendation network damage
Mistake value, returns penalty values at Classification Loss value.
8. a kind of device for output information, comprising:
Acquiring unit is configured to obtain the image of object to be detected；
Detection unit is configured to inputting described image into defects detection model trained in advance, it is corresponding to obtain described image
Defect classification and position, wherein the defects detection model uses Faster RCNN algorithm；
Output unit is configured to export the corresponding defect classification of described image and position.
9. device according to claim 8, wherein the acquiring unit is further configured to:
At least one image of object to be detected is obtained according at least one of following parameter:
Angle, filter, times mirror, focuses light.
10. device according to claim 8, wherein the object be wooden objects and the defect classification include with
It is at least one of lower:
Worm hole cracks, collapses scarce, incrustation.
11. the apparatus according to claim 1, wherein the defects detection model uses net based on SE-ResNet
Network.
12. the device according to one of claim 8-11, wherein described device further includes training unit, is configured to:
Training sample set is obtained, training sample includes the sample image of object to be detected, defect corresponding with sample image
Type and position；
It, will be corresponding with the sample image of input using the sample image of the training sample in the training sample set as input
As output, training obtains the defects detection model for defect type and position.
13. device according to claim 12, wherein the defects detection model, comprising: feature extraction layer, region push away
Recommend network, the pond ROI layer, classification recurrence layer.
14. device according to claim 12, wherein the penalty values of the defects detection model include region recommendation network
Penalty values, return penalty values at Classification Loss value.
15. a kind of electronic equipment, comprising:
One or more processors；
Storage device is stored thereon with 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 method as described in any in claim 1-7.
16. a kind of computer-readable medium, is stored thereon with computer program, wherein real when described program is executed by processor
The now method as described in any in claim 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910248452.4A CN110009614A (en) | 2019-03-29 | 2019-03-29 | Method and apparatus for output information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910248452.4A CN110009614A (en) | 2019-03-29 | 2019-03-29 | Method and apparatus for output information |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110009614A true CN110009614A (en) | 2019-07-12 |
Family
ID=67168878
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910248452.4A Pending CN110009614A (en) | 2019-03-29 | 2019-03-29 | Method and apparatus for output information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110009614A (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110458829A (en) * | 2019-08-13 | 2019-11-15 | 腾讯医疗健康(深圳)有限公司 | Image quality control method, device, equipment and storage medium based on artificial intelligence |
| CN110599469A (en) * | 2019-08-30 | 2019-12-20 | 中国铁道科学研究院集团有限公司 | Method and system for detecting defects of key parts of motor train unit and electronic equipment |
| CN110969600A (en) * | 2019-11-12 | 2020-04-07 | 华北电力大学扬中智能电气研究中心 | Product defect detection method and device, electronic equipment and storage medium |
| CN111062920A (en) * | 2019-12-13 | 2020-04-24 | 北京百度网讯科技有限公司 | Method and device for generating semiconductor detection report |
| CN111105413A (en) * | 2019-12-31 | 2020-05-05 | 哈尔滨工程大学 | Intelligent spark plug appearance defect detection system |
| CN111402220A (en) * | 2020-03-11 | 2020-07-10 | 北京百度网讯科技有限公司 | Method and device for acquiring information |
| CN111429894A (en) * | 2020-03-12 | 2020-07-17 | 南京邮电大学 | Many-to-many speaker conversion method based on SE-ResNet STARGAN |
| CN111627015A (en) * | 2020-05-29 | 2020-09-04 | 联想(北京)有限公司 | Small sample defect identification method, device, equipment and storage medium |
| WO2021249255A1 (en) * | 2020-06-12 | 2021-12-16 | 青岛理工大学 | Grabbing detection method based on rp-resnet |
| JP2022526473A (en) * | 2020-03-11 | 2022-05-25 | ベイジン バイドゥ ネットコム サイエンス テクノロジー カンパニー リミテッド | Methods and devices for acquiring information, electronic devices, storage media and computer programs |
| CN116481461A (en) * | 2022-11-24 | 2023-07-25 | 广州帕卡汽车零部件有限公司 | Method for detecting roughness of hole forming and notch of sound and heat insulation spare and accessory parts of automobile |
| CN111627015B (en) * | 2020-05-29 | 2024-04-26 | 联想(北京)有限公司 | Small sample defect identification method, device, equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108427920A (en) * | 2018-02-26 | 2018-08-21 | 杭州电子科技大学 | A kind of land and sea border defense object detection method based on deep learning |
| CN109087280A (en) * | 2018-07-02 | 2018-12-25 | 北京百度网讯科技有限公司 | Continuous casting billet quality detection method, device, control equipment, server and medium |
| CN109145973A (en) * | 2018-08-10 | 2019-01-04 | 北京百度网讯科技有限公司 | Method and apparatus for detecting monocrystaline silicon solar cell defect |
-
2019
- 2019-03-29 CN CN201910248452.4A patent/CN110009614A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108427920A (en) * | 2018-02-26 | 2018-08-21 | 杭州电子科技大学 | A kind of land and sea border defense object detection method based on deep learning |
| CN109087280A (en) * | 2018-07-02 | 2018-12-25 | 北京百度网讯科技有限公司 | Continuous casting billet quality detection method, device, control equipment, server and medium |
| CN109145973A (en) * | 2018-08-10 | 2019-01-04 | 北京百度网讯科技有限公司 | Method and apparatus for detecting monocrystaline silicon solar cell defect |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110458829A (en) * | 2019-08-13 | 2019-11-15 | 腾讯医疗健康(深圳)有限公司 | Image quality control method, device, equipment and storage medium based on artificial intelligence |
| CN110458829B (en) * | 2019-08-13 | 2024-01-30 | 腾讯医疗健康(深圳)有限公司 | Image quality control method, device, equipment and storage medium based on artificial intelligence |
| CN110599469A (en) * | 2019-08-30 | 2019-12-20 | 中国铁道科学研究院集团有限公司 | Method and system for detecting defects of key parts of motor train unit and electronic equipment |
| CN110969600A (en) * | 2019-11-12 | 2020-04-07 | 华北电力大学扬中智能电气研究中心 | Product defect detection method and device, electronic equipment and storage medium |
| CN111062920A (en) * | 2019-12-13 | 2020-04-24 | 北京百度网讯科技有限公司 | Method and device for generating semiconductor detection report |
| CN111062920B (en) * | 2019-12-13 | 2023-06-20 | 北京百度网讯科技有限公司 | Method and device for generating semiconductor detection report |
| CN111105413B (en) * | 2019-12-31 | 2021-05-14 | 哈尔滨工程大学 | Intelligent spark plug appearance defect detection system |
| CN111105413A (en) * | 2019-12-31 | 2020-05-05 | 哈尔滨工程大学 | Intelligent spark plug appearance defect detection system |
| WO2021179565A1 (en) * | 2020-03-11 | 2021-09-16 | 北京百度网讯科技有限公司 | Method and apparatus for acquiring information |
| JP2022526473A (en) * | 2020-03-11 | 2022-05-25 | ベイジン バイドゥ ネットコム サイエンス テクノロジー カンパニー リミテッド | Methods and devices for acquiring information, electronic devices, storage media and computer programs |
| CN111402220B (en) * | 2020-03-11 | 2023-06-09 | 北京百度网讯科技有限公司 | Method and device for acquiring information |
| CN111402220A (en) * | 2020-03-11 | 2020-07-10 | 北京百度网讯科技有限公司 | Method and device for acquiring information |
| CN111429894A (en) * | 2020-03-12 | 2020-07-17 | 南京邮电大学 | Many-to-many speaker conversion method based on SE-ResNet STARGAN |
| CN111627015A (en) * | 2020-05-29 | 2020-09-04 | 联想(北京)有限公司 | Small sample defect identification method, device, equipment and storage medium |
| CN111627015B (en) * | 2020-05-29 | 2024-04-26 | 联想(北京)有限公司 | Small sample defect identification method, device, equipment and storage medium |
| WO2021249255A1 (en) * | 2020-06-12 | 2021-12-16 | 青岛理工大学 | Grabbing detection method based on rp-resnet |
| CN116481461A (en) * | 2022-11-24 | 2023-07-25 | 广州帕卡汽车零部件有限公司 | Method for detecting roughness of hole forming and notch of sound and heat insulation spare and accessory parts of automobile |
| CN116481461B (en) * | 2022-11-24 | 2023-09-22 | 广州帕卡汽车零部件有限公司 | Method for detecting roughness of hole forming and notch of sound and heat insulation spare and accessory parts of automobile |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110009614A (en) | Method and apparatus for output information | |
| CN109993734A (en) | Method and apparatus for output information | |
| CN109949286A (en) | Method and apparatus for output information | |
| CN108805083B (en) | Single-stage video behavior detection method | |
| CN109978870A (en) | Method and apparatus for output information | |
| CN106471527B (en) | Study is localized from world model | |
| JP6596164B2 (en) | Unsupervised matching in fine-grained datasets for single view object reconstruction | |
| CN108776808A (en) | A kind of method and apparatus for detecting ladle corrosion defect | |
| CN109551459A (en) | Robot system and method for taking out work | |
| CN108830837A (en) | A kind of method and apparatus for detecting ladle corrosion defect | |
| CN108446585A (en) | Method for tracking target, device, computer equipment and storage medium | |
| CN107423398A (en) | Exchange method, device, storage medium and computer equipment | |
| CN109815770A (en) | Two-dimentional code detection method, apparatus and system | |
| CN109145759A (en) | Vehicle attribute recognition methods, device, server and storage medium | |
| CN111259779B (en) | Video motion detection method based on center point track prediction | |
| CN109145766A (en) | Model training method, device, recognition methods, electronic equipment and storage medium | |
| CN109409327B (en) | RRU module object pose detection method based on end-to-end deep neural network | |
| CN110517259A (en) | A kind of detection method, device, equipment and the medium of product surface state | |
| CN107004207A (en) | System and method for estimating removal of home using object measurement result | |
| CN109145836A (en) | Ship target video detection method based on deep learning network and Kalman filtering | |
| CN109886356A (en) | A kind of target tracking method based on three branch's neural networks | |
| CN110070106A (en) | Smog detection method, device and electronic equipment | |
| US20220366244A1 (en) | Modeling Human Behavior in Work Environment Using Neural Networks | |
| CN115376518B (en) | Voiceprint recognition method, system, equipment and medium for real-time noise big data | |
| CN112613569A (en) | Image recognition method, and training method and device of image classification model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |