CN111127454A - Method and system for generating industrial defect sample based on deep learning - Google Patents
Method and system for generating industrial defect sample based on deep learning Download PDFInfo
- Publication number
- CN111127454A CN111127454A CN201911376015.7A CN201911376015A CN111127454A CN 111127454 A CN111127454 A CN 111127454A CN 201911376015 A CN201911376015 A CN 201911376015A CN 111127454 A CN111127454 A CN 111127454A
- Authority
- CN
- China
- Prior art keywords
- defect
- industrial
- picture
- deep learning
- generating
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
The invention provides a method and a system for generating an industrial defect sample based on deep learning, which comprises the following steps: step 1: acquiring an industrial defect product picture, and marking defect information on the industrial defect product picture; step 2: constructing an confrontation generation depth model; and step 3: according to the labeled defect information, performing countermeasure training in an antibiotic formation depth model; and 4, step 4: training to obtain a defect sample; and 5: and screening the defective samples, and removing the defective samples which do not accord with the preset conditions to obtain industrial defective samples. The industrial defect picture generated by the invention has weak cross correlation, prominent defect characteristics and higher fine structure quality; the quality of the industrial defect picture generated by the method is high, and the performance index of the deep defect detection network trained according to the method can be obviously improved.
Description
Technical Field
The invention relates to the technical field of computer vision and deep learning, in particular to a method and a system for generating an industrial defect sample based on deep learning.
Background
In industrial production, almost all products need to be inspected, and most of the inspection processes are performed by visual inspection (hereinafter referred to as visual inspection) by quality inspectors, especially some surface defects such as decorative plates, metal surfaces, keyboard surfaces, etc., which is very common in actual industry. Because the variety of product, the variety of defect, for example, there are mar, stain, plaque, wearing and tearing, piece etc. promptly to the defect of dalle, greatly increased quality control person's work load and work degree of difficulty, lead to artifical visual inspection efficiency to descend and easily because the fatigue of quality control person and error lead to the condition such as lou examining, wrong detection, improve the time cost of production line and probably influence the quality of the product on market. With the development of deep learning technology, industrial defect detection technology based on deep learning is widely applied to the field of defect detection.
The essence of deep learning is that the characteristics are learned by constructing a machine learning model with multiple hidden layers and massive training data, so that the accuracy and universality of classification or prediction are finally improved. However, training an effective deep learning model requires a large amount of labeled data, a large amount of graphics card resources, and a long training time, and in many industrial scenarios, the acquisition cost of defect images is very high, so that the number of samples is very limited, and it is difficult to directly train the deep learning model. Therefore, the false detection rate and the missing detection rate of the trained defect detection model are high, and the requirements of enterprises are difficult to meet.
With the development of data enhancement technology, some scientific researchers have developed some technical schemes capable of improving data enhancement. Patent document CN108932735A discloses a method of generating deep learning samples, which proposes to generate some deep learning samples using poisson distribution. However, because only the traditional noise-like method is used, the quality of the generated industrial defect samples is not high, that is, the cross correlation between the industrial defect samples is stronger, and the degree of improving the detection capability of the deep learning defect detection model is smaller. Other anti-generation techniques are also often plagued by problems of pattern collapse, overfitting, etc., and it is difficult to generate high quality industrial defect samples. The existing patent technology is a method for generating high-quality industrial defect samples, and the requirement of an actual enterprise on training of an automatic defect detection system based on a deep learning network is difficult to solve.
Patent document CN109559298A (application number: 201811357765.5) discloses a method for detecting a defect of a lotion pump based on deep learning, which is to respectively construct classification models of various angles based on the principles of transfer learning and convolutional neural network in deep learning to detect a defect sample. First, the network model was pre-trained using the Mini-ImageNet dataset. And then, adjusting the model structure and loading parameters of a pre-training network, inputting the training set and the verification set of each angle of the emulsion pump into a convolutional neural network for training after an image preprocessing algorithm, automatically performing the processes of feature extraction and classification in the network, and adjusting the network hyper-parameters according to the change of the accuracy rate of the verification set in the training process to obtain a final network model. And finally, inputting the preprocessed emulsion pump test sample into the trained model, and detecting the defect identification effect of the final model.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method and a system for generating an industrial defect sample based on deep learning.
The method for generating the industrial defect sample based on the deep learning provided by the invention comprises the following steps:
step 1: acquiring an industrial defect product picture, and marking defect information on the industrial defect product picture;
step 2: constructing an confrontation generation depth model;
and step 3: according to the labeled defect information, performing countermeasure training in an antibiotic formation depth model;
and 4, step 4: training to obtain a defect sample;
and 5: and screening the defective samples, and removing the defective samples which do not accord with the preset conditions to obtain industrial defective samples.
Preferably, an error function is adopted in the confrontation training of step 3, and the formula is as follows:
wherein Gn is a generator network, Dn is a discriminator network, Ladv is a countermeasure loss function, Lrec is a reconstruction loss function, and a is a fixed coefficient and is a reconstruction error weight.
Preferably, the defect information includes marking data of a defect location.
Preferably, the construction of the contrast generating depth model is performed according to the image samples;
and image sampling is carried out according to the labeled data, so that the sampling probability is improved.
Preferably, the countermeasure generation depth model comprises a generator network and a discriminator network;
the generator network comprises one or more fully connected layers, and one or more volume blocks; the full-connection layer maps the uniform distribution or Gaussian distribution into a certain specific distribution and generates an industrial defect picture with the resolution ratio conforming to a first set range; each convolution block comprises one or more convolution layers and one-time up-sampling operation, an input industrial defect picture with the resolution conforming to a first set range is added with noise to generate an industrial defect picture with the resolution conforming to a second set range, semantic content on the industrial defect picture with the resolution conforming to the second set range is enriched, and a high-definition picture which is mapped into industrial defects by a generator network is obtained;
the discriminator network comprises one or more convolutional blocks, each convolutional block comprising one or more convolutional layers; and distinguishing the industrial defective product picture from the defect picture generated by the generator network.
The system for generating the industrial defect sample based on the deep learning provided by the invention comprises the following steps:
module M1: acquiring an industrial defect product picture, and marking defect information on the industrial defect product picture;
module M2: constructing an confrontation generation depth model;
module M3: according to the labeled defect information, performing countermeasure training in an antibiotic formation depth model;
module M4: training to obtain a defect sample;
module M5: and screening the defective samples, and removing the defective samples which do not accord with the preset conditions to obtain industrial defective samples.
Preferably, the module M3 uses an error function in the confrontation training, and the formula is:
wherein Gn is a generator network, Dn is a discriminator network, Ladv is a countermeasure loss function, Lrec is a reconstruction loss function, and a is a fixed coefficient and is a reconstruction error weight.
Preferably, the defect information includes marking data of a defect location.
Preferably, the construction of the contrast generating depth model is performed according to the image samples;
and image sampling is carried out according to the labeled data, so that the sampling probability is improved.
Preferably, the countermeasure generation depth model comprises a generator network and a discriminator network;
the generator network comprises one or more fully connected layers, and one or more volume blocks; the full-connection layer maps the uniform distribution or Gaussian distribution into a certain specific distribution and generates an industrial defect picture with the resolution ratio conforming to a first set range; each convolution block comprises one or more convolution layers and one-time up-sampling operation, an input industrial defect picture with the resolution conforming to a first set range is added with noise to generate an industrial defect picture with the resolution conforming to a second set range, semantic content on the industrial defect picture with the resolution conforming to the second set range is enriched, and a high-definition picture which is mapped into industrial defects by a generator network is obtained;
the discriminator network comprises one or more convolutional blocks, each convolutional block comprising one or more convolutional layers; and distinguishing the industrial defective product picture from the defect picture generated by the generator network.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, a neural network is generated by training countermeasures based on a small number of image samples with industrial defects, and a large number of image samples with industrial defects can be generated in a short time by means of manual discrimination;
2. the industrial defect picture generated by the invention has weak cross correlation, prominent defect characteristics and higher fine structure quality;
3. the quality of the industrial defect picture generated by the method is high, and the performance index of the deep defect detection network trained according to the method can be obviously improved.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of a high quality industrial defect sample of the present invention;
FIG. 2 is a network architecture diagram of a generator network and an arbiter network employed in the present invention;
FIG. 3 is a diagram of a high quality industrial sample of a steel sheet blister defect of the present invention;
FIG. 4 is a diagram of a high quality industrial sample of an aluminum sheet blistering defect of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
As shown in fig. 1, which is a flow chart of an embodiment of the present invention, the method includes the following steps:
step A: collecting a small amount of real industrial defect samples, and marking the positions of the defects on the real industrial defect samples
And B: according to fig. 2, a countermeasure generation depth model is constructed.
And C: for the depth model, the labeled data is utilized to carry out countermeasure training
Step D: executing generator network in trained model to generate defect sample
Step E: manually discriminating the defect sample generated by the generator network, and eliminating the over-distorted picture generated by the generator to obtain the high-quality industrial defect sample
As shown in fig. 2, the countermeasure generation depth model is composed of a generator network and a discriminator network.
Wherein the generator network comprises one or more fully connected layers, and one or more volume blocks. The full-connection layer maps the uniform probability distribution (or Gaussian distribution) into a certain specific distribution and generates an industrial defect picture with lower resolution. Each convolution block comprises one or more convolution layers, and an up-sampling operation can add low-resolution pictures input into the convolution block and noise to generate a picture with higher resolution and enrich semantic content on the picture. Finally, the uniform probability distribution (or gaussian distribution, etc.) is mapped by the generator network into a high definition picture of the industrial defect.
Wherein the network of discriminators comprises one or more volume blocks. Each convolution block includes one or more convolution layers. Their role is to gradually reduce whether the input picture is a real defect picture or a defect picture generated by the generator network.
The training method adopts countertraining, and the error function adopted by the countertraining is as follows:
wherein Gn is a generator network, Dn is a discriminator network, Ladv is a countermeasure loss function, Lrec is a reconstruction loss function, and a is a fixed coefficient called reconstruction error weight.
The opposition loss function is a measure of the ability of the discriminator network to distinguish between real industrial defect samples and industrial defect samples generated by the generator network. When the discriminator network is optimized, the error function is improved as much as possible, so that the discriminator network can more accurately distinguish samples generated by the generator network. The error function is reduced as much as possible while optimizing the generator network, thereby enabling the generator network to better spoof the arbiter network. The antagonistic training can improve the ability of the generator network to generate as realistic industrial defects as possible.
The reconstruction loss function measures the ability of the generator network to map a particular input to a real industrial defect. Defined as the root mean square value of the pixel-by-pixel deviation of the defect image and the real defect image output by the generator network, with all noise inputs of the generator network set to zero. The reconstruction loss function can improve the fine structure quality of the generated picture.
When the size of the processed image is trained to be a rolling block which is closer to the size of a real industrial defect image, in order to avoid video memory overflow, a small-picture sampling training method is adopted to calculate and resist generation errors. The method can obviously reduce the video memory occupation of the training process under the condition of not reducing the training quality too much. However, the method of small-frame sampling training cannot be adopted when the reconstruction error is calculated, otherwise style imbalance is caused.
When sampling a small image, the probability of sampling each position of the original image by using the interested region can be controlled. That is, the defect location identified in step a is selected as the region of interest, and the sampling probability in this region is increased. The method can obviously improve the generation quality of the defect part, enrich the semantic information of the defect part and improve the training speed.
When the generator network is actually used for generating the high-quality industrial defect picture, the whole generator network can be used, or only the pure convolution part of the generator network can be used without using the full-connection layer part of the generator network according to the generation effect. When the full connection layer is not used, the industrial defect picture with low resolution needs to be input to the position of the pure convolution layer input of the generator network, so that the industrial defect picture with high quality is generated.
Bubble defects often occur in the field of steel plate defect detection, high-quality sample generation of the bubble defects of the steel plates is achieved by the aid of the technology, convex skin defects often occur in the field of aluminum plate defect detection, and high-quality sample generation of the convex skin defects of the aluminum plates is achieved by the aid of the technology, and the technology is respectively shown in fig. 3 and fig. 4.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A method for generating industrial defect samples based on deep learning is characterized by comprising the following steps:
step 1: acquiring an industrial defect product picture, and marking defect information on the industrial defect product picture;
step 2: constructing an confrontation generation depth model;
and step 3: according to the labeled defect information, performing countermeasure training in an antibiotic formation depth model;
and 4, step 4: training to obtain a defect sample;
and 5: and screening the defective samples, and removing the defective samples which do not accord with the preset conditions to obtain industrial defective samples.
2. The method for generating industrial defect samples based on deep learning as claimed in claim 1, wherein the step 3 employs an error function in the confrontation training, and the formula is:
wherein Gn is a generator network, Dn is a discriminator network, Ladv is a countermeasure loss function, Lrec is a reconstruction loss function, and a is a fixed coefficient and is a reconstruction error weight.
3. The method for generating industrial defect samples based on deep learning of claim 1, wherein the defect information comprises labeling data of defect locations.
4. The method for generating the industrial defect sample based on the deep learning as claimed in claim 3, wherein the construction of the antagonistic generation depth model is carried out according to the image sampling;
and image sampling is carried out according to the labeled data, so that the sampling probability is improved.
5. The method for generating industrial defect samples based on deep learning of claim 1, wherein the countermeasure generation depth model comprises a generator network and a discriminator network;
the generator network comprises one or more fully connected layers, and one or more volume blocks; the full-connection layer maps the uniform distribution or Gaussian distribution into a certain specific distribution and generates an industrial defect picture with the resolution ratio conforming to a first set range; each convolution block comprises one or more convolution layers and one-time up-sampling operation, an input industrial defect picture with the resolution conforming to a first set range is added with noise to generate an industrial defect picture with the resolution conforming to a second set range, semantic content on the industrial defect picture with the resolution conforming to the second set range is enriched, and a high-definition picture which is mapped into industrial defects by a generator network is obtained;
the discriminator network comprises one or more convolutional blocks, each convolutional block comprising one or more convolutional layers; and distinguishing the industrial defective product picture from the defect picture generated by the generator network.
6. A system for generating industrial defect samples based on deep learning, comprising:
module M1: acquiring an industrial defect product picture, and marking defect information on the industrial defect product picture;
module M2: constructing an confrontation generation depth model;
module M3: according to the labeled defect information, performing countermeasure training in an antibiotic formation depth model;
module M4: training to obtain a defect sample;
module M5: and screening the defective samples, and removing the defective samples which do not accord with the preset conditions to obtain industrial defective samples.
7. The system for generating industrial defect samples based on deep learning as claimed in claim 1, wherein the module M3 adopts an error function in the countermeasure training, and the formula is as follows:
wherein Gn is a generator network, Dn is a discriminator network, Ladv is a countermeasure loss function, Lrec is a reconstruction loss function, and a is a fixed coefficient and is a reconstruction error weight.
8. The system for generating industrial defect samples based on deep learning of claim 1, wherein the defect information comprises annotation data of defect locations.
9. The system for generating industrial defect samples based on deep learning as claimed in claim 8, wherein the construction of the antagonistic generation depth model is performed according to image sampling;
and image sampling is carried out according to the labeled data, so that the sampling probability is improved.
10. The system for generating industrial defect samples based on deep learning of claim 1, wherein the countermeasure generation depth model comprises a generator network and a discriminator network;
the generator network comprises one or more fully connected layers, and one or more volume blocks; the full-connection layer maps the uniform distribution or Gaussian distribution into a certain specific distribution and generates an industrial defect picture with the resolution ratio conforming to a first set range; each convolution block comprises one or more convolution layers and one-time up-sampling operation, an input industrial defect picture with the resolution conforming to a first set range is added with noise to generate an industrial defect picture with the resolution conforming to a second set range, semantic content on the industrial defect picture with the resolution conforming to the second set range is enriched, and a high-definition picture which is mapped into industrial defects by a generator network is obtained;
the discriminator network comprises one or more convolutional blocks, each convolutional block comprising one or more convolutional layers; and distinguishing the industrial defective product picture from the defect picture generated by the generator network.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911376015.7A CN111127454A (en) | 2019-12-27 | 2019-12-27 | Method and system for generating industrial defect sample based on deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911376015.7A CN111127454A (en) | 2019-12-27 | 2019-12-27 | Method and system for generating industrial defect sample based on deep learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111127454A true CN111127454A (en) | 2020-05-08 |
Family
ID=70503882
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911376015.7A Pending CN111127454A (en) | 2019-12-27 | 2019-12-27 | Method and system for generating industrial defect sample based on deep learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111127454A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113298190A (en) * | 2021-07-05 | 2021-08-24 | 四川大学 | Weld image recognition and classification algorithm based on large-size unbalanced samples |
| CN113658108A (en) * | 2021-07-22 | 2021-11-16 | 西南财经大学 | Glass defect detection method based on deep learning |
| CN113793333A (en) * | 2021-11-15 | 2021-12-14 | 常州微亿智造科技有限公司 | Defect picture generation method and device applied to industrial quality inspection |
| CN115272310A (en) * | 2022-09-26 | 2022-11-01 | 江苏智云天工科技有限公司 | Method and device for detecting defects of workpiece |
| CN115943429A (en) * | 2020-08-03 | 2023-04-07 | 科磊股份有限公司 | Depth-generating models for optical or other mode selection |
Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107220600A (en) * | 2017-05-17 | 2017-09-29 | 清华大学深圳研究生院 | A kind of Picture Generation Method and generation confrontation network based on deep learning |
| CN108171762A (en) * | 2017-12-27 | 2018-06-15 | 河海大学常州校区 | System and method for is reconfigured quickly in a kind of similar image of the compressed sensing of deep learning |
| CN108198179A (en) * | 2018-01-03 | 2018-06-22 | 华南理工大学 | A kind of CT medical image pulmonary nodule detection methods for generating confrontation network improvement |
| CN108492265A (en) * | 2018-03-16 | 2018-09-04 | 西安电子科技大学 | CFA image demosaicing based on GAN combines denoising method |
| CN108764005A (en) * | 2018-01-31 | 2018-11-06 | 华侨大学 | A kind of high-spectrum remote sensing atural object space Spectral Characteristic extracting method and system |
| CN108961217A (en) * | 2018-06-08 | 2018-12-07 | 南京大学 | A kind of detection method of surface flaw based on positive example training |
| CN109087243A (en) * | 2018-06-29 | 2018-12-25 | 中山大学 | A kind of video super-resolution generation method generating confrontation network based on depth convolution |
| CN109146988A (en) * | 2018-06-27 | 2019-01-04 | 南京邮电大学 | Non-fully projection CT image rebuilding method based on VAEGAN |
| CN109242841A (en) * | 2018-08-30 | 2019-01-18 | 广东工业大学 | A kind of transmission tower defect inspection method based on generation confrontation network |
| CN109360188A (en) * | 2018-09-13 | 2019-02-19 | 西南交通大学 | A kind of detection method of high iron catenary equipotential line defective mode |
| CN109544656A (en) * | 2018-11-23 | 2019-03-29 | 南京信息工程大学 | A kind of compressed sensing image rebuilding method and system based on generation confrontation network |
| CN109559276A (en) * | 2018-11-14 | 2019-04-02 | 武汉大学 | A kind of image super-resolution rebuilding method based on reference-free quality evaluation and characteristic statistics |
| CN109584337A (en) * | 2018-11-09 | 2019-04-05 | 暨南大学 | A kind of image generating method generating confrontation network based on condition capsule |
| CN109933677A (en) * | 2019-02-14 | 2019-06-25 | 厦门一品威客网络科技股份有限公司 | Image generating method and image generation system |
| CN110322433A (en) * | 2019-05-27 | 2019-10-11 | 苏州佳赛特智能科技有限公司 | A kind of data set amplification method towards the detection of appearance defective vision |
| CN110320162A (en) * | 2019-05-20 | 2019-10-11 | 广东省智能制造研究所 | A kind of semi-supervised high-spectral data quantitative analysis method based on generation confrontation network |
| CN110461001A (en) * | 2019-07-31 | 2019-11-15 | 天津大学 | The method that confrontation model estimates RSS missing values is generated based on adaptive context |
| CN110570396A (en) * | 2019-08-07 | 2019-12-13 | 华中科技大学 | industrial product defect detection method based on deep learning |
| CN110598792A (en) * | 2019-09-16 | 2019-12-20 | 福州大学 | Drainage pipeline defect detection training data generation method based on PGGAN transfer learning |
-
2019
- 2019-12-27 CN CN201911376015.7A patent/CN111127454A/en active Pending
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107220600A (en) * | 2017-05-17 | 2017-09-29 | 清华大学深圳研究生院 | A kind of Picture Generation Method and generation confrontation network based on deep learning |
| CN108171762A (en) * | 2017-12-27 | 2018-06-15 | 河海大学常州校区 | System and method for is reconfigured quickly in a kind of similar image of the compressed sensing of deep learning |
| CN108198179A (en) * | 2018-01-03 | 2018-06-22 | 华南理工大学 | A kind of CT medical image pulmonary nodule detection methods for generating confrontation network improvement |
| CN108764005A (en) * | 2018-01-31 | 2018-11-06 | 华侨大学 | A kind of high-spectrum remote sensing atural object space Spectral Characteristic extracting method and system |
| CN108492265A (en) * | 2018-03-16 | 2018-09-04 | 西安电子科技大学 | CFA image demosaicing based on GAN combines denoising method |
| CN108961217A (en) * | 2018-06-08 | 2018-12-07 | 南京大学 | A kind of detection method of surface flaw based on positive example training |
| CN109146988A (en) * | 2018-06-27 | 2019-01-04 | 南京邮电大学 | Non-fully projection CT image rebuilding method based on VAEGAN |
| CN109087243A (en) * | 2018-06-29 | 2018-12-25 | 中山大学 | A kind of video super-resolution generation method generating confrontation network based on depth convolution |
| CN109242841A (en) * | 2018-08-30 | 2019-01-18 | 广东工业大学 | A kind of transmission tower defect inspection method based on generation confrontation network |
| CN109360188A (en) * | 2018-09-13 | 2019-02-19 | 西南交通大学 | A kind of detection method of high iron catenary equipotential line defective mode |
| CN109584337A (en) * | 2018-11-09 | 2019-04-05 | 暨南大学 | A kind of image generating method generating confrontation network based on condition capsule |
| CN109559276A (en) * | 2018-11-14 | 2019-04-02 | 武汉大学 | A kind of image super-resolution rebuilding method based on reference-free quality evaluation and characteristic statistics |
| CN109544656A (en) * | 2018-11-23 | 2019-03-29 | 南京信息工程大学 | A kind of compressed sensing image rebuilding method and system based on generation confrontation network |
| CN109933677A (en) * | 2019-02-14 | 2019-06-25 | 厦门一品威客网络科技股份有限公司 | Image generating method and image generation system |
| CN110320162A (en) * | 2019-05-20 | 2019-10-11 | 广东省智能制造研究所 | A kind of semi-supervised high-spectral data quantitative analysis method based on generation confrontation network |
| CN110322433A (en) * | 2019-05-27 | 2019-10-11 | 苏州佳赛特智能科技有限公司 | A kind of data set amplification method towards the detection of appearance defective vision |
| CN110461001A (en) * | 2019-07-31 | 2019-11-15 | 天津大学 | The method that confrontation model estimates RSS missing values is generated based on adaptive context |
| CN110570396A (en) * | 2019-08-07 | 2019-12-13 | 华中科技大学 | industrial product defect detection method based on deep learning |
| CN110598792A (en) * | 2019-09-16 | 2019-12-20 | 福州大学 | Drainage pipeline defect detection training data generation method based on PGGAN transfer learning |
Non-Patent Citations (3)
| Title |
|---|
| CHRISTIAN LEDIG 等: "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network", 《ARXIV:1609.04802V1》 * |
| HAODONG ZHANG 等: "Weld Defect Detection Based on Deep Learning Method", 《2019 IEEE 15TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE)》 * |
| YIKUAN YU 等: "Point Encoder GAN: A deep learning model for 3D point cloud inpainting", 《NEUROCOMPUTING》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115943429A (en) * | 2020-08-03 | 2023-04-07 | 科磊股份有限公司 | Depth-generating models for optical or other mode selection |
| CN115943429B (en) * | 2020-08-03 | 2024-04-05 | 科磊股份有限公司 | Depth generation model for optical or other mode selection |
| CN113298190A (en) * | 2021-07-05 | 2021-08-24 | 四川大学 | Weld image recognition and classification algorithm based on large-size unbalanced samples |
| CN113658108A (en) * | 2021-07-22 | 2021-11-16 | 西南财经大学 | Glass defect detection method based on deep learning |
| CN113793333A (en) * | 2021-11-15 | 2021-12-14 | 常州微亿智造科技有限公司 | Defect picture generation method and device applied to industrial quality inspection |
| WO2023082760A1 (en) * | 2021-11-15 | 2023-05-19 | 常州微亿智造科技有限公司 | Defective picture generation method and apparatus applied to industrial quality inspection |
| US11783474B1 (en) | 2021-11-15 | 2023-10-10 | Changzhou Microintelligence Co., Ltd. | Defective picture generation method and apparatus applied to industrial quality inspection |
| CN115272310A (en) * | 2022-09-26 | 2022-11-01 | 江苏智云天工科技有限公司 | Method and device for detecting defects of workpiece |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111179251B (en) | Defect detection system and method based on twin neural network and by utilizing template comparison | |
| CN111127454A (en) | Method and system for generating industrial defect sample based on deep learning | |
| CN101667245B (en) | Human face detection method by cascading novel detection classifiers based on support vectors | |
| CN111681240A (en) | Bridge surface crack detection method based on YOLO v3 and attention mechanism | |
| CN111611998A (en) | Adaptive feature block extraction method based on candidate region area and width and height | |
| CN111932511B (en) | Electronic component quality detection method and system based on deep learning | |
| Yu et al. | Detecting gear surface defects using background‐weakening method and convolutional neural network | |
| CN104200478B (en) | Low-resolution touch screen image defect detection method based on sparse representation | |
| CN114638784A (en) | Method and device for detecting surface defects of copper pipe based on FE-YOLO | |
| CN110009622B (en) | Display panel appearance defect detection network and defect detection method thereof | |
| CN117011274A (en) | Automatic glass bottle detection system and method thereof | |
| CN111161237A (en) | Fruit and vegetable surface quality detection method, storage medium and sorting device thereof | |
| CN115775236A (en) | Surface tiny defect visual detection method and system based on multi-scale feature fusion | |
| CN111145145A (en) | Image surface defect detection method based on MobileNet | |
| CN116129242A (en) | Aluminum product surface defect identification method based on improved YOLOv4 | |
| CN113506281B (en) | Bridge crack detection method based on deep learning framework | |
| CN114494250A (en) | Floor defect detection method based on neural network model | |
| Azizah et al. | Tajweed-YOLO: Object detection method for Tajweed by applying HSV color model augmentation on Mushaf images | |
| CN110633739A (en) | Polarizer defect image real-time classification method based on parallel module deep learning | |
| CN116704208B (en) | Local interpretable method based on characteristic relation | |
| CN206897873U (en) | A kind of image procossing and detecting system based on detection product performance | |
| CN111179278B (en) | Image detection method, device, equipment and storage medium | |
| CN112633327B (en) | Staged metal surface defect detection method, system, medium, equipment and application | |
| Chen et al. | RDUnet-A: A Deep Neural Network Method with Attention for Fabric Defect Segmentation Based on Autoencoder | |
| Xie et al. | Defect detection of printed circuit board based on small target recognition network |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200508 |