CN106778791A - A kind of timber visual identity method based on multiple perceptron - Google Patents
A kind of timber visual identity method based on multiple perceptron Download PDFInfo
- Publication number
- CN106778791A CN106778791A CN201710118330.4A CN201710118330A CN106778791A CN 106778791 A CN106778791 A CN 106778791A CN 201710118330 A CN201710118330 A CN 201710118330A CN 106778791 A CN106778791 A CN 106778791A
- Authority
- CN
- China
- Prior art keywords
- timber
- target
- perceptron
- visual identity
- method based
- 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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Multimedia (AREA)
- Image Analysis (AREA)
Abstract
The present invention provides a kind of timber visual identity method based on multiple perceptron, it is characterized in that, by setting up multilayer perceptron Mathematical Modeling, wood feature addition Sample Storehouse is carried out into learning training, after the completion of training, system can carry out pattern-recognition according to the feature for learning, view-based access control model system to the characteristic vector of new input.By above-mentioned machine-learning process, system constantly can extract wood feature from Sample Storehouse, and automatic discrimination is carried out to timber kind, and the automation to timber processing loading and unloading part is implemented in combination with other mechanical structures.
Description
Technical field
The present invention relates to depth learning technology field, more particularly to a kind of timber visual identity side based on multiple perceptron
Method.
Background technology
During deep processing is carried out to timber, the different wood raw material of N kinds generally occurs in producing line.Due to every kind of
Timber suffers from its special texture, and conventional plant generally uses the method that artificial experience judges when classifying to timber,
It is less efficient and be likely to occur and be difficult to the problems such as relating, it is impossible to realize streamlined operation.This problem is solved, it is necessary to build one
The categorizing system of individual automation.Based on machine learning, it is possible to constantly extract wood feature from Sample Storehouse, to timber kind
Class carries out automatic discrimination, and the automation to timber processing loading and unloading part is implemented in combination with other mechanical structures.
The content of the invention
To realize above-mentioned technical purpose, above-mentioned technique effect is reached, the present invention is achieved through the following technical solutions：A kind of base
In the timber visual identity method of multiple perceptron, it is characterised in that comprise the following steps：
Step 1：Founding mathematical models simultaneously carry out sample training, so as to generate multilayer perceptron parameter；
Step 2：Target enters cog region, and wood surface is taken pictures, and extracts clarification of objective value；
Step 3：Characteristic value is input into multilayer perceptron, is identified, if target can not be recognized, carry out abnormality processing, protected
Found exceptional sample is stayed, new samples are added in database, new training sample set of laying equal stress on, it is ensured that the similar spy that next time occurs
Different situation can be accurately identified；If target can be recognized, result to executing agency is sent；
Step 4：Executing agency carries out sort operation to target according to the result for receiving, and return to step 2 is ready to carry out down
The sort instructions of one target.
Wherein, the Mathematical Modeling of the multilayer perceptron includes input layer, hidden layer and output layer, wherein, the input
Layer includes system input, predominantly object feature value；Hidden layer includes weighted sum, excitation function.
The clarification of objective value is extracted and is captured and calculated by vision system, carries out image procossing to timber first,
Then many texture informations based on gray scale are extracted by calculating the gray level co-occurrence matrixes of timber.
The clarification of objective value is extracted and is captured and calculated by vision system, carries out image procossing to timber first,
Then many texture informations based on gray scale are extracted by calculating the gray level co-occurrence matrixes of timber.
The situation of prior art is different from, the beneficial effects of the invention are as follows：By automated system, information system and regard
The fusion of feel system, the system eliminates the process of manual sort's timber, improves the accuracy of production efficiency and classification, while
Or quality is related there is provided guarantee.
The method cost of implementation is low, and with very strong flexibility and scalability, the accuracy of whole system can be in operation
During improve constantly.The enterprise related for timber processing, the method can for its a set of feature based data are provided can
The discrimination standard of quantization.Enterprise can be on this system realized from growth according to the actual conditions of oneself, in reduction human cost
The economic worth that producing line is brought is improved simultaneously.
Brief description of the drawings
Fig. 1 is the system flow chart of timber visual identity method of the present invention based on multiple perceptron.
Fig. 2 is the Mathematical Modeling of multilayer perceptron of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
Referring to Fig. 1, there is provided a kind of timber visual identity method based on multiple perceptron, it is characterised in that including as follows
Step：
Step 1：Founding mathematical models simultaneously carry out sample training, so as to generate multilayer perceptron parameter；
Step 2：Target enters cog region, and wood surface is taken pictures, and extracts clarification of objective value；
Step 3：Characteristic value is input into multilayer perceptron, is identified, if target can not be recognized, carry out abnormality processing, protected
Found exceptional sample is stayed, new samples are added in database, new training sample set of laying equal stress on, it is ensured that the similar spy that next time occurs
Different situation can be accurately identified；If target can be recognized, result to executing agency is sent；
Step 4：Executing agency carries out sort operation to target according to the result for receiving, and return to step 2 is ready to carry out down
The sort instructions of one target.
Specifically,
The Mathematical Modeling of the multilayer perceptron includes input layer, hidden layer and output layer, specifically, input layer is mainly wrapped
System input is included, herein i.e. characteristic vector.Hidden layer includes the contents such as weighted sum, excitation function, the output of last layer
Used as next layer of input, can finally adjust out one group of linear coefficient w and b, and this tuning process can be traditional BP algorithm
Or other optimized algorithms.Linear result of calculation is produced output by the processing unit of hidden layer by nonlinear activation function.Finally
Result would generally use Function Mapping to 0, between 1, that is, provide the probability of every kind of classification.The generation of hidden layer and processing unit is
The process being trained to the input sample of known output, training result is one group of parameter, is usually implemented as file and is stored in meter
In calculation machine system.Training sample is more, then system classifies more accurate to clarification of objective.Factory can be to existing before online implementing
Product is trained, and produces corresponding multilayer perceptron training file.The clarification of objective value is extracted by vision system capture simultaneously
Calculate, image procossing is carried out to timber first, then extract many by calculating the gray level co-occurrence matrixes of timber
Texture information based on gray scale, what is extracted states texture information including target energy, correlation, unfavourable balance away from, contrast and image
Clarification of objective value described in entropy is extracted and is captured and calculated by vision system, can be envisioned as an one-dimensional vector of 1*M
FV=[X0,X1,X2….Xm], these features can be timber entirety or local feature.Important for one, timber is characterized in it
Texture.Its texture feature vector is obtained, first has to carry out image procossing to timber.By the gray scale symbiosis square for calculating timber
Battle array, we can therefrom extract many useful informations on texture.
Gray level co-occurrence matrixes are two pixel gray scales i, j on θ directions at a distance of d, i.e. (x, y) and (x+a, x+b) in image
The matrix that probability element p (i, j) of appearance is constituted.By gray matrix, we can extract many textures based on gray scale
Information, resulting texture information include energy, correlation, unfavourable balance away from, contrast, image entropy, while having an anisotropy that, ash
Degree histogram, colouring information etc., these characteristic values carry out spy as the mark to material by the K sample to the timber that has no guts
Extraction is levied, we most obtain N × K characteristic vector at last, altogether N × K × M feature, for same timber, their spy
Levying vector has similitude, and for different timber, their characteristic vector relevance will be reduced, meanwhile, in order to avoid occurring
The problem of over-fitting and dimension disaster, feature M should not take excessively.
Through the above way, whole system is in the process of running have very strong autgmentability and self-optimized ability,
Constantly during operation, the identification accuracy of system can be improved constantly, even if due to certain special case of certain type timber
There is accidental None- identified or identification mistake, being run into equally be can guarantee that after wood feature addition Sample Storehouse so in next time
Can be accurately identified during special circumstances.And due to just having carried out enough sample trainings before system startup, such situation occurs
Probability is inherently very low, and system can be with stable operation in the starting stage.
Embodiments of the invention are the foregoing is only, the scope of the claims of the invention is not thereby limited, it is every to utilize this hair
Equivalent structure or equivalent flow conversion that bright specification and accompanying drawing content are made, or direct or brief introduction are used in other correlation techniques
Field, is included within the scope of the present invention.
Claims (4)
1. a kind of timber visual identity method based on multiple perceptron, it is characterised in that comprise the following steps：
Step 1：Founding mathematical models simultaneously carry out sample training, so as to generate multilayer perceptron parameter；
Step 2：Target enters cog region, and wood surface is taken pictures, and extracts clarification of objective value；
Step 3：Characteristic value is input into multilayer perceptron, is identified, if target can not be recognized, carry out abnormality processing, retain institute
It was found that exceptional sample, new samples are added in database, new training sample set of laying equal stress on, it is ensured that next time occur similar special feelings
Condition can be accurately identified；If target can be recognized, result to executing agency is sent；
Step 4：Executing agency carries out sort operation to target according to the result for receiving, and return to step 2 is ready to carry out next mesh
Target sort instructions.
2. a kind of timber visual identity method based on multiple perceptron according to claim 1, it is characterised in that：It is described
The Mathematical Modeling of multilayer perceptron includes input layer, hidden layer and output layer, wherein, the input layer is input into including system, main
It to be object feature value；Hidden layer includes weighted sum, excitation function.
3. a kind of timber visual identity method based on multiple perceptron according to claim 1, it is characterised in that：It is described
Clarification of objective value is extracted and is captured and calculated by vision system, carries out image procossing to timber first, then by calculating
The gray level co-occurrence matrixes of timber are so as to extract many texture informations based on gray scale.
4. feature extracting method according to claim 3, it is characterised in that：The texture information includes target energy, phase
Guan Xing, unfavourable balance are away from, contrast and image entropy.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710118330.4A CN106778791A (en) | 2017-03-01 | 2017-03-01 | A kind of timber visual identity method based on multiple perceptron |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710118330.4A CN106778791A (en) | 2017-03-01 | 2017-03-01 | A kind of timber visual identity method based on multiple perceptron |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106778791A true CN106778791A (en) | 2017-05-31 |
Family
ID=58960745
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710118330.4A Pending CN106778791A (en) | 2017-03-01 | 2017-03-01 | A kind of timber visual identity method based on multiple perceptron |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106778791A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107967491A (en) * | 2017-12-14 | 2018-04-27 | 北京木业邦科技有限公司 | Machine learning method, device, electronic equipment and the storage medium again of plank identification |
| CN109145955A (en) * | 2018-07-26 | 2019-01-04 | 中国林业科学研究院木材工业研究所 | A kind of Wood Identification Method and system |
| CN109724543A (en) * | 2018-12-29 | 2019-05-07 | 南京林业大学 | A kind of method of Fast Evaluation wood milling processing performance |
| CN111208138A (en) * | 2020-02-28 | 2020-05-29 | 天目爱视（北京）科技有限公司 | Intelligent wood recognition device |
| CN111695498A (en) * | 2020-06-10 | 2020-09-22 | 西南林业大学 | Wood identity detection method |
| CN113610184A (en) * | 2021-08-19 | 2021-11-05 | 江西应用技术职业学院 | Wood texture classification method based on transfer learning |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101140216A (en) * | 2007-08-08 | 2008-03-12 | 东北电力大学 | Gas-liquid two-phase flow type recognition method based on digital graphic processing technique |
| CN101393137A (en) * | 2008-10-24 | 2009-03-25 | 胡伟 | Appearance quality detection method for products, system and detection device |
| CN101398894A (en) * | 2008-06-17 | 2009-04-01 | 浙江师范大学 | Automobile license plate automatic recognition method and implementing device thereof |
| CN102034098A (en) * | 2010-12-21 | 2011-04-27 | 上海电机学院 | Automatic bolt sorting system and automatic bolt sorting method |
| CN102323213A (en) * | 2011-08-25 | 2012-01-18 | 华北电力大学 | On-line recognition method for flame free radial based fuel type |
| CN102509113A (en) * | 2011-11-08 | 2012-06-20 | 浙江大学 | Brain tumor MIB-1 index range detection method |
| CN102812357A (en) * | 2009-11-26 | 2012-12-05 | 马来西亚理工大学 | Methods And System For Recognizing Wood Species |
| CN105772407A (en) * | 2016-01-26 | 2016-07-20 | 耿春茂 | Waste classification robot based on image recognition technology |
| CN105825224A (en) * | 2016-03-10 | 2016-08-03 | 浙江生辉照明有限公司 | Obtaining method and apparatus of classifier family |
| CN106156768A (en) * | 2016-07-01 | 2016-11-23 | 屈桢深 | The vehicle registration certificate detection method of view-based access control model |
| CN106373127A (en) * | 2016-09-14 | 2017-02-01 | 东北林业大学 | Laser scanning parallel detection method for wood species and surface defects |
| CN106415602A (en) * | 2016-08-25 | 2017-02-15 | 深圳市锐明技术股份有限公司 | Method and device for detecting paired lane lines |
-
2017
- 2017-03-01 CN CN201710118330.4A patent/CN106778791A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101140216A (en) * | 2007-08-08 | 2008-03-12 | 东北电力大学 | Gas-liquid two-phase flow type recognition method based on digital graphic processing technique |
| CN101398894A (en) * | 2008-06-17 | 2009-04-01 | 浙江师范大学 | Automobile license plate automatic recognition method and implementing device thereof |
| CN101393137A (en) * | 2008-10-24 | 2009-03-25 | 胡伟 | Appearance quality detection method for products, system and detection device |
| CN102812357A (en) * | 2009-11-26 | 2012-12-05 | 马来西亚理工大学 | Methods And System For Recognizing Wood Species |
| CN102034098A (en) * | 2010-12-21 | 2011-04-27 | 上海电机学院 | Automatic bolt sorting system and automatic bolt sorting method |
| CN102323213A (en) * | 2011-08-25 | 2012-01-18 | 华北电力大学 | On-line recognition method for flame free radial based fuel type |
| CN102509113A (en) * | 2011-11-08 | 2012-06-20 | 浙江大学 | Brain tumor MIB-1 index range detection method |
| CN105772407A (en) * | 2016-01-26 | 2016-07-20 | 耿春茂 | Waste classification robot based on image recognition technology |
| CN105825224A (en) * | 2016-03-10 | 2016-08-03 | 浙江生辉照明有限公司 | Obtaining method and apparatus of classifier family |
| CN106156768A (en) * | 2016-07-01 | 2016-11-23 | 屈桢深 | The vehicle registration certificate detection method of view-based access control model |
| CN106415602A (en) * | 2016-08-25 | 2017-02-15 | 深圳市锐明技术股份有限公司 | Method and device for detecting paired lane lines |
| CN106373127A (en) * | 2016-09-14 | 2017-02-01 | 东北林业大学 | Laser scanning parallel detection method for wood species and surface defects |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107967491A (en) * | 2017-12-14 | 2018-04-27 | 北京木业邦科技有限公司 | Machine learning method, device, electronic equipment and the storage medium again of plank identification |
| CN109145955A (en) * | 2018-07-26 | 2019-01-04 | 中国林业科学研究院木材工业研究所 | A kind of Wood Identification Method and system |
| CN109145955B (en) * | 2018-07-26 | 2019-10-22 | 中国林业科学研究院木材工业研究所 | A kind of Wood Identification Method and system |
| CN109724543A (en) * | 2018-12-29 | 2019-05-07 | 南京林业大学 | A kind of method of Fast Evaluation wood milling processing performance |
| CN111208138A (en) * | 2020-02-28 | 2020-05-29 | 天目爱视（北京）科技有限公司 | Intelligent wood recognition device |
| CN111695498A (en) * | 2020-06-10 | 2020-09-22 | 西南林业大学 | Wood identity detection method |
| CN113610184A (en) * | 2021-08-19 | 2021-11-05 | 江西应用技术职业学院 | Wood texture classification method based on transfer learning |
| CN113610184B (en) * | 2021-08-19 | 2022-03-11 | 江西应用技术职业学院 | Wood texture classification method based on transfer learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106778791A (en) | A kind of timber visual identity method based on multiple perceptron | |
| CN108717568B (en) | A kind of image characteristics extraction and training method based on Three dimensional convolution neural network | |
| CN104573731B (en) | Fast target detection method based on convolutional neural networks | |
| CN109800628A (en) | A kind of network structure and detection method for reinforcing SSD Small object pedestrian detection performance | |
| CN109299716A (en) | Training method, image partition method, device, equipment and the medium of neural network | |
| CN109508655A (en) | The SAR target identification method of incomplete training set based on twin network | |
| CN107844784A (en) | Face identification method, device, computer equipment and readable storage medium storing program for executing | |
| CN110363253A (en) | A kind of Surfaces of Hot Rolled Strip defect classification method based on convolutional neural networks | |
| CN106504064A (en) | Clothes classification based on depth convolutional neural networks recommends method and system with collocation | |
| CN103824054A (en) | Cascaded depth neural network-based face attribute recognition method | |
| CN104504007B (en) | The acquisition methods and system of a kind of image similarity | |
| CN110321862B (en) | Pedestrian re-identification method based on compact ternary loss | |
| CN106651915A (en) | Target tracking method of multi-scale expression based on convolutional neural network | |
| CN109145944B (en) | Classification method based on longitudinal three-dimensional image deep learning features | |
| CN109871892A (en) | A kind of robot vision cognitive system based on small sample metric learning | |
| CN104298974A (en) | Human body behavior recognition method based on depth video sequence | |
| CN109858476A (en) | The extending method and electronic equipment of label | |
| CN104050460B (en) | The pedestrian detection method of multiple features fusion | |
| CN110414626A (en) | A kind of pig variety ecotype method, apparatus and computer readable storage medium | |
| CN114140665A (en) | Dense small target detection method based on improved YOLOv5 | |
| CN114492634B (en) | Fine granularity equipment picture classification and identification method and system | |
| CN116649191A (en) | Remote fertilization and irrigation control system and method based on PLC | |
| Mathew et al. | Determining the region of apple leaf affected by disease using YOLO V3 | |
| CN111160428A (en) | Automatic vegetable identification method based on CNN-SVM algorithm | |
| CN106326914A (en) | SVM-based pearl multi-classification method |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20191129 Address after: 610041 5 / F, zone B, building 3, No.200, Tianfu 5th Street, Chengdu hi tech Zone, China (Sichuan) pilot Free Trade Zone, Chengdu, Sichuan Province Applicant after: Chengdu Tianheng Intelligent Manufacturing Technology Co., Ltd. Address before: 610213 Shuangliu County Economic Development Zone, Southwest Airlines Development Zone, Chengdu, Sichuan, Chengdu, Chengdu University of Electronic Science and technology of China Applicant before: Chengdu Science and Technology Ltd. of Tian Heng electricity section |
|
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170531 |