CN108363961A - Bridge pad disease recognition method based on transfer learning between convolutional neural networks - Google Patents
Bridge pad disease recognition method based on transfer learning between convolutional neural networks Download PDFInfo
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Abstract
A kind of bridge pad disease recognition method based on transfer learning between convolutional neural networks of the present invention, includes the following steps：Bridge pad disease photo is obtained, and label information is assigned for every photo；With the method for image procossing, increase the data volume for training convolutional neural networks；All pictures in training set and test set are scaled to the cromogram of predefined size, and carry out image preprocessing；Obtain trained convolutional neural networks model on other data sets；In a manner of knowledge migration, the convolutional neural networks model with automatic identification bridge pad disease function is obtained.A kind of bridge pad disease automatic identifying method based on convolutional neural networks of the present invention, by knowledge migration pattern drill convolutional neural networks in precision, there is apparent advantage in convergence rate and greatly reduce the data volume needed for trained neural network, the bridge pad disease of and data complicated for disease scene not easily collecting has certain realistic meaning.
Description
Technical field
The present invention relates to civil engineering and artificial intelligence crossing domain, specifically a kind of bridge based on convolutional neural networks
Bearing damage automatic identifying method.
Background technology
With the fast development of China's infrastructure construction in recent years, building industry development is rapid, a large amount of road and bridge
Construction finishes, and the thing followed is late detection and maintenance work.Bridge pad is the important structure for connecting bridge upper and lower part structure
Part is to count for much where the throat of a bridge block, once there is disease, does not such as find and handle in time, will influence structure
Stress and traffic safety.The detection work main path or artificial detection of bridge pad at present, this method take,
Arduously and traffic can be influenced.Some build remote mountains in, marine bridge is difficult to be realized by the method for artificial detection, or be difficult
Ensure the safety of bridge machinery personnel.Therefore, there is an urgent need for a kind of automatic testing methods of bridge pad disease.
Convolutional neural networks are one kind of artificial neural network, have become the research hotspot in present image identification field.
The shared network structure of its weights is allowed to more similar and biological neural network, reduces the quantity of weights.Convolutional neural networks
Input of the multidimensional image as neural network can be directly used, feature extraction sum number complicated in tional identification algorithm is avoided
According to reconstruction process and there is higher recognition accuracy.
The research of Matthew D.Zeiler et al. illustrates convolutional neural networks and learns to be characterized in layering, a width
Image always provides network inputs with pixel value array form, and first layer learns to be characterized as marginal information；Network it is higher
The basic pattern that layer detection marginal information rearranges in a specific manner；The higher of network combines basic pattern pair
Answer the component of typical objects；This means some rudimentary spies that the convolutional neural networks of this training on other data sets learn
Sign can also move in the identification mission of bridge pad disease.
Invention content
Goal of the invention：In order to overcome the deficiencies in the prior art, the present invention to provide a kind of based on convolutional neural networks
Bridge pad disease automatic identifying method.
Technical solution：In order to solve the above technical problems, a kind of bridge pad disease based on convolutional neural networks of the present invention
Evil automatic identifying method, includes the following steps：
S1：Bridge pad disease photo is obtained, and label information is assigned for every photo, label information corresponds to photo institute
The bridge pad Damage Types of category；
S2：With the method for image procossing, increase the data volume for training convolutional neural networks；
S3：The bridge pad disease photo of acquisition is divided into training set and test set, training set is for training convolutional nerve
Network, test set are used for test network, and all pictures in training set and test set are scaled to the cromogram of predefined size,
And carry out image preprocessing；
S4：Trained convolutional neural networks model on other data sets is obtained, and according to bridge pad
The requirement of disease recognition adjusts the output layer node number of the convolutional neural networks；
S5：In a manner of knowledge migration, with pretreated bridge pad photo training convolutional neural networks are passed through, ladder is used
Degree declines the weights that full articulamentum is adjusted with error backpropagation algorithm, obtains having automatic identification bridge pad disease function
Convolutional neural networks model.
In step S1, the data set of bearing damage photo should include the disease of multiple and different types.
In step S2, the amount of images for training convolutional neural networks is increased by the method for image procossing, for carrying
The generalization ability of high neural network.
In step S3, the size of picture is uniformly scaled to the picture that pixel size is 224 × 224.
In step S3, the preprocess method of image is：Calculate the sum of pixel value of all images then divided by image number
Measure the pixel value for subtracting the mean value image in every piece image to a mean value image.
In step S4, the advance trained model of acquisition is trained classical on ImageNet data sets
VGG16 convolutional neural networks models.
In step S4, the output layer node number of the convolutional neural networks is adjusted according to the requirement of bridge pad disease recognition
Refer to the classification number for the bridge pad disease that adjustment its network Softmax output layers are classified for N number of node, N expression needs,
Softmax layers of calculation formula are as follows：
Wherein, SiFor i-th of input only of this layer, SkFor k-th of net input of the layer.
In step S5, the method for carrying out knowledge migration to neural network is：By trained model is longitudinally drawn in advance
It is divided into each sub-network, when using bridge pad disease photo training, only training adjusts the weights of last full articulamentum and ties up
The weights for holding other each sub-networks are constant.
In step S5, gradient descent method the specific steps are：The gradient of each weights of counting loss function pair, from any point
Start, the negative direction along the gradient moves a distance, continues along gradient reverse direction operation a distance, in this way in new position
The weights of continuous update network.
In step S5, back-propagation algorithm the specific steps are：Convolutional Neural net is being updated using gradient descent method iteration
When the weights of each layer of network, gradient is propagated forward according to chain type Rule for derivation from last layer of network successively.
Advantageous effect：A kind of bridge pad disease automatic identifying method based on convolutional neural networks of the present invention, has
Following advantageous effect：
There is by knowledge migration pattern drill convolutional neural networks in precision, convergence rate apparent advantage and big
Reduce the data volume needed for trained neural network greatly, the bridge pad disease of and data complicated for disease scene not easily collecting
With certain realistic meaning.
Description of the drawings
Fig. 1 is proposed by the present invention a kind of based on the bridge pad disease for carrying out knowledge migration between different convolutional neural networks
The flow chart of automatic identifying method；
Fig. 2 is the longitudinally divided sub-network structure schematic diagrames of VGG16 that knowledge migration is carried out in the present invention.
Specific implementation mode
The invention will be further described below in conjunction with the accompanying drawings.
As shown in Figure 1, a kind of bridge pad disease automatic identifying method based on convolutional neural networks, including following step
Suddenly：
S1：Bridge pad disease photo is obtained, and label information is assigned for every photo, label information corresponds to photo institute
The bridge pad Damage Types of category；
S2：With the method for image procossing, the pixel values of the different color channels of such as random adjustment photo, affine transformation,
Flip horizontal, flip vertical etc. increase the data volume for training convolutional neural networks；
S3：The bridge pad disease photo of acquisition is divided into training set and test set, training set is for training convolutional nerve
Network, test set are used for test network, and all pictures in training set and test set are scaled to the cromogram of predefined size,
And carry out image preprocessing；
S4：Trained convolutional neural networks model on other data sets is obtained, and according to bridge pad
The requirement of disease recognition adjusts the output layer node number of the convolutional neural networks；
S5：In a manner of knowledge migration, with pretreated bridge pad photo training convolutional neural networks are passed through, ladder is used
Degree declines the weights that full articulamentum is adjusted with error backpropagation algorithm, obtains having automatic identification bridge pad disease function
Convolutional neural networks model.
In step S1, the data set of bearing damage photo should include the disease of multiple and different types.
In step S2, the amount of images for training convolutional neural networks is increased by the method for image procossing, for carrying
The generalization ability of high neural network.
In step S3, the size of picture uniformly scales the picture that pixel value is 224 × 224.
In step S3, the preprocess method of image is：Calculate the sum of pixel value of all images then divided by image number
Measure the pixel value for subtracting the mean value image in every piece image to a mean value image.
In step S4, the advance trained model of acquisition is trained classical on ImageNet data sets
VGG16 convolutional neural networks models, the structure chart of VGG16 network models are as shown in Figure 2.
In step S4, the output layer node number of the convolutional neural networks is adjusted according to the requirement of bridge pad disease recognition
Refer to the classification number for the bridge pad disease that adjustment its network Softmax output layers are classified for N number of node, N expression needs,
Softmax layers of calculation formula are as follows：
Wherein, SiFor i-th of input only of this layer, SkFor k-th of net input of the layer.
In step S5, the method for carrying out knowledge migration to neural network is：By trained model is longitudinally drawn in advance
It is divided into each sub-network, when using bridge pad disease photo training, only training adjusts the weights of last full articulamentum and ties up
The weights for holding other each sub-networks are constant.
In step S5, gradient descent method the specific steps are：The gradient of each weights of counting loss function pair, from any point
Start, the negative direction along the gradient moves a distance, continues along gradient reverse direction operation a distance, in this way in new position
The weights of continuous update network.
In step S5, back-propagation algorithm the specific steps are：Convolutional Neural net is being updated using gradient descent method iteration
When the weights of each layer of network, gradient is propagated forward according to chain type Rule for derivation from last layer of network successively.
The above is only a preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (10)
1. a kind of bridge pad disease automatic identifying method based on convolutional neural networks, which is characterized in that include the following steps：
S1：Bridge pad disease photo is obtained, and label information is assigned for every photo, label information corresponds to belonging to photo
Bridge pad Damage Types；
S2：With the method for image procossing, increase the data volume for training convolutional neural networks；
S3：The bridge pad disease photo of acquisition is divided into training set and test set, training set is used for training convolutional neural networks,
Test set is used for test network, and all pictures in training set and test set is scaled to the cromogram of predefined size, goes forward side by side
Row image preprocessing；
S4：Trained convolutional neural networks model on other data sets is obtained, and according to bridge pad disease
The requirement of identification adjusts the output layer node number of the convolutional neural networks；
S5：In a manner of knowledge migration, with pretreated bridge pad photo training convolutional neural networks are passed through, using under gradient
Drop adjusts the weights of full articulamentum with error backpropagation algorithm, obtains the convolution with automatic identification bridge pad disease function
Neural network model.
2. the bridge pad disease automatic identifying method according to claim 1 based on convolutional neural networks, feature exist
In：In step S1, the data set of bearing damage photo should include the disease of multiple and different types.
3. the bridge pad disease automatic identifying method according to claim 1 based on convolutional neural networks, feature exist
In：In step S2, the amount of images for training convolutional neural networks is increased by the method for image procossing, for improving nerve
The generalization ability of network.
4. the bridge pad disease automatic identifying method according to claim 1 based on convolutional neural networks, feature exist
In：In step S3, the size of picture is uniformly scaled to the picture that pixel size is 224 × 224.
5. the bridge pad disease automatic identifying method according to claim 1 based on convolutional neural networks, feature exist
In：In step S3, the preprocess method of image is：It calculates the sum of pixel value of all images then divided by the quantity of image obtains
One mean value image subtracts the pixel value of the mean value image in every piece image.
6. the bridge pad disease automatic identifying method according to claim 1 based on convolutional neural networks, feature exist
In：In step S4, the advance trained model of acquisition is the VGG16 of trained classics on ImageNet data sets
Convolutional neural networks model.
7. the bridge pad disease automatic identifying method according to claim 1 based on convolutional neural networks, feature exist
In：In step S4, the output layer node number that the convolutional neural networks are adjusted according to the requirement of bridge pad disease recognition refers to
It is N number of node to adjust its network Softmax output layers, and N indicates the classification number for the bridge pad disease for needing to classify,
Softmax layers of calculation formula are as follows：
Wherein, SiFor i-th of input only of this layer, SkFor k-th of net input of the layer.
8. the bridge pad disease automatic identifying method according to claim 1 based on convolutional neural networks, feature exist
In：In step S5, the method for carrying out knowledge migration to neural network is：It is each by trained model is longitudinally divided in advance
A sub-network, when using bridge pad disease photo training, only training adjusts the weights of last full articulamentum and maintains other
The weights of each sub-network are constant.
9. the bridge pad disease automatic identifying method according to claim 1 based on convolutional neural networks, feature exist
In：In step S5, gradient descent method the specific steps are：The gradient of each weights of counting loss function pair, since any point,
Negative direction along the gradient moves a distance, continues along gradient reverse direction operation a distance in new position, continuous in this way
Update network weights.
10. the bridge pad disease automatic identifying method according to claim 1 based on convolutional neural networks, feature exist
In：In step S5, back-propagation algorithm the specific steps are：Each layer of convolutional neural networks is being updated using gradient descent method iteration
Weights when, gradient is propagated forward according to chain type Rule for derivation from last layer of network successively.
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| CN109191445A (en) * | 2018-08-29 | 2019-01-11 | 极创智能（北京）健康科技有限公司 | Bone deformation analytical method based on artificial intelligence |
| CN109508650A (en) * | 2018-10-23 | 2019-03-22 | 浙江农林大学 | A kind of wood recognition method based on transfer learning |
| CN109753566A (en) * | 2019-01-09 | 2019-05-14 | 大连民族大学 | The model training method of cross-cutting sentiment analysis based on convolutional neural networks |
| CN109784438A (en) * | 2018-12-28 | 2019-05-21 | 福建华闽通达信息技术有限公司 | A kind of bridge maintenance disease record, identification and treatment measures guidance method and system |
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| CN109191445A (en) * | 2018-08-29 | 2019-01-11 | 极创智能（北京）健康科技有限公司 | Bone deformation analytical method based on artificial intelligence |
| CN109508650A (en) * | 2018-10-23 | 2019-03-22 | 浙江农林大学 | A kind of wood recognition method based on transfer learning |
| CN109784438A (en) * | 2018-12-28 | 2019-05-21 | 福建华闽通达信息技术有限公司 | A kind of bridge maintenance disease record, identification and treatment measures guidance method and system |
| CN110457982A (en) * | 2018-12-28 | 2019-11-15 | 中国科学院合肥物质科学研究院 | A kind of crop disease image-recognizing method based on feature transfer learning |
| CN110457982B (en) * | 2018-12-28 | 2023-04-11 | 中国科学院合肥物质科学研究院 | Crop disease image identification method based on feature migration learning |
| CN109753566B (en) * | 2019-01-09 | 2020-11-24 | 大连民族大学 | Model training method for cross-domain emotion analysis based on convolutional neural network |
| CN109753566A (en) * | 2019-01-09 | 2019-05-14 | 大连民族大学 | The model training method of cross-cutting sentiment analysis based on convolutional neural networks |
| CN109800806A (en) * | 2019-01-14 | 2019-05-24 | 中山大学 | A kind of corps diseases detection algorithm based on deep learning |
| CN110261108A (en) * | 2019-01-18 | 2019-09-20 | 北京化工大学 | Bearing fault method of identification when specified operating based on CNN color property figure |
| CN109978847A (en) * | 2019-03-19 | 2019-07-05 | 东南大学 | Drag-line housing disease automatic identifying method based on transfer learning Yu drag-line robot |
| CN109978847B (en) * | 2019-03-19 | 2023-07-04 | 东南大学 | Automatic inhaul cable sleeve disease identification method based on transfer learning and inhaul cable robot |
| CN110555511A (en) * | 2019-07-24 | 2019-12-10 | 北京踏歌智行科技有限公司 | Method, device, electronic equipment and computer readable storage medium for identifying area |
| CN110766045A (en) * | 2019-09-12 | 2020-02-07 | 深圳大学 | Underground drainage pipeline disease identification method, intelligent terminal and storage medium |
| CN110766045B (en) * | 2019-09-12 | 2023-07-07 | 深圳大学 | Underground drainage pipeline disease identification method, intelligent terminal and storage medium |
| CN111191714A (en) * | 2019-12-28 | 2020-05-22 | 浙江大学 | Intelligent identification method for bridge appearance damage diseases |
| CN112749733A (en) * | 2020-11-27 | 2021-05-04 | 江西省交通科学研究院 | Bridge disease identification method based on mixed model and image pyramid |
| CN112699736A (en) * | 2020-12-08 | 2021-04-23 | 江西省交通科学研究院 | Bridge bearing fault identification method based on space attention |
| CN112699736B (en) * | 2020-12-08 | 2024-06-07 | 江西省交通科学研究院有限公司 | Bridge bearing disease identification method based on spatial attention |
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