CN110532872A - A kind of landslide hierarchy system and method based on convolution supporting vector neural network - Google Patents
A kind of landslide hierarchy system and method based on convolution supporting vector neural network Download PDFInfo
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Abstract
The present invention provides a kind of landslide hierarchy system and method based on convolution supporting vector neural network, belong to subgrade engineering field.A kind of landslide hierarchy system based on convolution supporting vector neural network, including the image storage module for storing acquired image, for obtaining the image collection module of image data, the image processing module of Classification of Landslides differentiation is carried out to image, described image processing module includes the image classification unit for identifying image with the presence or absence of the image discriminating unit on landslide and for carrying out landslide classification to image.By collecting image, and image is analyzed to judge landslide classification, saves manpower, it is high-efficient, without security risk.
Description
Technical field
The present invention relates to subgrade engineering fields, relate generally to a kind of landslide classification based on convolution supporting vector neural network
System and method.
Background technique
Past highway because grade it is low, it is linear it is poor, roadbed is not wide, excavate not depth, influence of the slope stability to safety is not
Significantly, people's Slope Stability does not cause enough attention still, with the development of the development of the national economy, communication
Make rapid progress, grade is higher and higher, and inevitable builds the feelings of high-grade highway to high embankment under MODEL OVER COMPLEX TOPOGRAPHY
Condition is increasing, and both at home and abroad there are many example of major landslip, landslide not only influences traffic safety, or even buries, interrupts
Traffic forces and abandons built use, causes immeasurable economic loss, the Study of Landslides origin cause of formation and prevention and treatment, has compeled in eyebrow
Eyelash.
In the prior art, common landslide classification needs manually to in-site measurement landslide soil amount, i.e. landslide classification；It is existing
The resources such as the usual labor intensive of method, equipment and time and the security risk of engineering staff can be brought, therefore this kind of existed with manpower
The method role that work location carries out landslide classification is limited.
Summary of the invention
The object of the present invention is to provide a kind of landslide stage divisions based on convolution supporting vector neural network, pass through collection
Image, and image is analyzed to judge landslide classification, manpower is saved, it is high-efficient, without security risk.
The above-mentioned technical purpose of the present invention is achieved through the following technical solutions, and one kind being based on convolution supporting vector nerve net
The landslide hierarchy system of network, including the image storage module for storing acquired image, for obtaining the image of image data
Module is obtained, the image processing module of Classification of Landslides differentiation is carried out to image, described image processing module includes for identifying figure
As the image discriminating unit with the presence or absence of landslide and the image classification unit for carrying out landslide classification to image.
As a preference of the present invention, described image judgement unit simultaneously differentiates image with described image taxon
And classification.As a preference of the present invention, a kind of landslide stage division based on convolution supporting vector neural network, including following step
It is rapid:
Step S1, collect image, and according to image be made whether exist landslide classification differentiation, if it exists landslide classification, then into
Pedestrian's work mark, until step S2；
Step S2, the image differentiated in step S1 is normalized, the image data after normalized is inputted
Convolution supporting vector nerve network system carries out disaggregated model training；
Step S3, whether identification and classification model accuracy meets the requirements, and requires if not meeting differentiation, convolution supporting vector nerve net
Network system adjust automatically optimizes convolution supporting vector neural network node weight, and return step S1, requires if meeting differentiation, institute
Training the disaggregated model come is optimal classification model；
Step S4, image to be detected is obtained, the differentiation of landslide classification is carried out using optimal classification model, according to convolution supporting vector
The probit value for the landslide grade that nerve network system provides selects the classification of maximum of probability, obtains classification achievement.
In the technical scheme, collected point image to be detected landslide classification is carried out with optimal classification model to differentiate,
Mainly image to be detected is put into convolution supporting vector nerve network system and calculate the classification of image to be detected landslide
The probability of type, and landslide hierarchical type of the maximum landslide hierarchical type of select probability as the image to be detected.It is specific main
If being sampled by the target area in image to be detected, it is put into the input layer of neural network, is supported vector layer
Obtain the probability of each label, the i.e. real value in section [0,1].
As a preference of the present invention, convolution supporting vector nerve network system is carrying out disaggregated model instruction according to step S2
When practicing, and carry out the differentiation of image grading.
As a preference of the present invention, landslide probit value includes large landslide probit value, major landslip probability according to step S4
Value, medium-sized landslide probit value, small-sized landslide probit value, the large landslide probit value, the major landslip probit value, it is described in
Type landslide probit value, the sum of described small-sized landslide probit value are 1.
In the technical scheme, Landslides are divided into large landslide, major landslip, medium-sized landslide, small-sized landslide, and totally 4 kinds
The landslide grade labelling of type, i.e. 4 data, this 4 data and be equal to 1, then, by obtained each image to be detected
The probability of label is averaged, and the probability of the label of image to be detected is obtained, and the maximum label of select probability is to be detected as this
The label of the landslide hierarchical type of image.
As a preference of the present invention, the normalized includes the meter for carrying out comentropy mean value to all image pixels
The characteristic value between -1 to 1 is obtained after calculation.
As a preference of the present invention, convolution supporting vector neural network structure includes input layer, and convolutional layer, active coating, under
Sample level normalizes layer and supporting vector layer.
As a preference of the present invention, input layer is the transverse and longitudinal coordinate and color of image of image.
Compared with prior art, the present invention has the following advantages:
1, using manually image is differentiated and classified with computer mutually matched mode, study precision is improved.
2, directly image is differentiated and is classified, save manpower, it is high-efficient, without security risk.
Detailed description of the invention
Fig. 1 is present system flow chart.
Specific embodiment
Present invention will be described in further detail below with reference to the accompanying drawings.
This specific embodiment is only explanation of the invention, is not limitation of the present invention, those skilled in the art
Member can according to need the modification that not creative contribution is made to the present embodiment after reading this specification, but as long as at this
All by the protection of Patent Law in the scope of the claims of invention.
A kind of landslide hierarchy system based on convolution supporting vector neural network according to figure 1, including for storing
The image storage module of acquired image carries out Classification of Landslides to image and sentences for obtaining the image collection module of image data
Other image processing module, described image processing module include for identify image with the presence or absence of landslide image discriminating unit with
And the image classification unit for carrying out landslide classification to image.Described image judgement unit and described image taxon are simultaneously
Image is differentiated and is classified.
A kind of landslide stage division based on convolution supporting vector neural network, comprising the following steps:
Step S1, collect image, and according to image be made whether exist landslide classification differentiation, if it exists landslide classification, then into
Pedestrian's work mark, until step S2；
Step S2, the image differentiated in step S1 is normalized, the image data after normalized is inputted
Convolution supporting vector nerve network system carries out disaggregated model training；
Step S3, whether identification and classification model accuracy meets the requirements, and requires if not meeting differentiation, convolution supporting vector nerve net
Network system adjust automatically optimizes convolution supporting vector neural network node weight, and return step S1, requires if meeting differentiation, institute
Training the disaggregated model come is optimal classification model；
Step S4, image to be detected is obtained, the differentiation of landslide classification is carried out using optimal classification model, according to convolution supporting vector
The probit value for the landslide grade that nerve network system provides selects the classification of maximum of probability, obtains classification achievement.
In the technical scheme, collected point image to be detected landslide classification is carried out with optimal classification model to differentiate,
Mainly image to be detected is put into convolution supporting vector nerve network system and calculate the classification of image to be detected landslide
The probability of type, and landslide hierarchical type of the maximum landslide hierarchical type of select probability as the image to be detected.It is specific main
If being sampled by the target area in image to be detected, it is put into the input layer of neural network, is supported vector layer
Obtain the probability of each label, the i.e. real value in section [0,1].
Normalized obtains the feature between -1 to 1 after referring to the calculating for carrying out comentropy mean value to all image pixels
Value.
According to step S2, convolution supporting vector nerve network system carries out image point when carrying out disaggregated model training
The differentiation of grade.The image pattern differentiated input convolution supporting vector nerve network system is carried out to the training of disaggregated model, it is main
If learning to convolutional neural networks system is brought into the image pattern manually marked；Repeat " training -> adjustment network
The process of structure -> retraining " is until differentiating correct.
According to step S3, the subjective setting value required as user is differentiated, if differentiating, accuracy is greater than user's subjectivity and sets
Definite value then meets differentiation and requires to differentiate that accuracy is less than subjective setting value, then do not meet differentiation and require.
According to step S4, the probit value that comes down includes large landslide probit value, major landslip probit value, medium-sized landslide probability
Value, small-sized landslide probit value, the large landslide probit value, the major landslip probit value, the medium-sized landslide probit value, institute
Stating the sum of small-sized landslide probit value is 1.
In the technical scheme, Landslides are divided into large landslide, major landslip, medium-sized landslide, small-sized landslide, and totally 4 kinds
The landslide grade labelling of type, i.e. 4 data, this 4 data and be equal to 1, then, by obtained each image to be detected
The probability of label is averaged, and the probability of the label of image to be detected is obtained, and the maximum label of select probability is to be detected as this
The label of the landslide hierarchical type of image.
Normalized includes that the calculating of comentropy mean value is carried out to all image datas, by all figures manually marked
Sample point after all being normalized divided by comentropy mean value as the space length of data.
Convolution supporting vector neural network structure includes input layer, convolutional layer, active coating, down-sampling layer, normalization layer and
Supporting vector layer.
Input layer is the transverse and longitudinal coordinate and color of image of image.
Convolutional layer:
1) size of core must be odd number, and no more than the width or height of this layer input；
2) transverse and longitudinal coordinate is not changed when intermediate representation passes through convolutional layer.
Active coating:
1) active coating does not change the transverse and longitudinal coordinate and color of image of convolutional layer expression；
2) activation primitive used in active coating includes but is not limited to following type function:
F (x)=l/(1+e-x)
F (x)=a*tanh (b*x), a, b are any non-zero real
f(x)= max(0,x)
f(x)= min(a,max(0,x))
f(x)= log(l+ex)
f(x)= |x|
f(x)= x2
f(x)= √X
f(x)= ax+b
3) active coating is followed after convolutional layer.
Down-sampling layer:
1) down-sampling layer does not change the color of image of intermediate representation；
2) down-sampling layer is the size of core to the drawdown ratio of image: i.e. core is that the down-sampling layer of m x n will cause intermediate representation
It is reduced into one layer (1/m) x (1/n), theoretically m and n can be random natural number.For example, 15x15x32 under 3x3 by adopting
After sample, become 5x5x32;After the down-sampling that 15x15x32 passes through 5x5, become 3x3x32;But 15x15x32 not can be carried out 2x2
Down-sampling because 15 cannot be divided exactly by 2；Be not to say that, input size must be 2 power, i.e., 16,32,64 etc., input ruler
As long as very little guarantee to be sampled by all down-sampling layers.
Normalize layer:
1) normalization layer does not change any size of intermediate representation；
2) normalization layer is necessary in the present invention, and addition normalization layer improves precision in this regular meeting and increases calculation amount.
- as combination be: convolutional layer -> active coating -> down-sampling layer -> normalization layer.
Following situations is special:
1) layer is smaller to precision improvement when but increasing many operands for addition normalization, cancels normalization layer, i.e., using following
Combination: convolutional layer -> active coating -> down-sampling layer；
2) normalization layer shift to an earlier date, effect is essentially identical, that is, use following combination: convolutional layer -> active coating -> normalization layer -> under adopt
Sample layer.
3) cancel down-sampling layer: convolutional layer -> active coating；Or convolutional layer -> active coating -> normalization layer；Down-sampling essence
Be in order to
Increase robustness, while being reduced the effect of the operand of succeeding layer；Several layers of down-samplings are usually had in one network, but
Not all " convolutional layer -> active coating " below will be with down-sampling.
Claims (8)
1. a kind of landslide hierarchy system based on convolution supporting vector neural network, which is characterized in that including being adopted for storing
The image storage module for collecting image carries out Classification of Landslides differentiation to image for obtaining the image collection module of image data
Image processing module, described image processing module include the image discriminating unit and use for identifying image with the presence or absence of landslide
In the image classification unit for carrying out landslide classification to image.
2. a kind of landslide hierarchy system based on convolution supporting vector neural network according to claim 1, feature exist
In described image judgement unit is differentiated and classified to image simultaneously with described image taxon.
3. a kind of landslide stage division based on convolution supporting vector neural network, which comprises the steps of:
Step S1, collect image, and according to image be made whether exist landslide classification differentiation, if it exists landslide classification, then into
Pedestrian's work mark, until step S2；
Step S2, the image differentiated in step S1 is normalized, the image data after normalized is inputted
Convolution supporting vector nerve network system carries out disaggregated model training；
Step S3, whether identification and classification model accuracy meets the requirements, and requires if not meeting differentiation, convolution supporting vector nerve net
Network system adjust automatically optimizes convolution supporting vector neural network node weight, and return step S1, requires if meeting differentiation, institute
Training the disaggregated model come is optimal classification model；
Step S4, image to be detected is obtained, the differentiation of landslide classification is carried out using optimal classification model, according to convolution supporting vector
The probit value for the landslide grade that nerve network system provides selects the classification of maximum of probability, obtains classification achievement.
4. a kind of landslide stage division based on convolution supporting vector neural network according to claim 3, feature exist
According to step S2, convolution supporting vector nerve network system carries out image grading when carrying out disaggregated model training
Differentiate.
5. a kind of landslide stage division based on convolution supporting vector neural network according to claim 3, feature exist
According to step S4, the probit value that comes down includes large landslide probit value, major landslip probit value, and medium-sized landslide probit value is small-sized
Come down probit value, the large landslide probit value, the major landslip probit value, and the medium-sized landslide probit value is described small-sized
The sum of the probit value that comes down is 1.
6. a kind of landslide stage division based on convolution supporting vector neural network according to claim 3, feature exist
In the normalized obtains the feature between -1 to 1 after referring to the calculating for carrying out comentropy mean value to the image pixel of image
Value.
7. a kind of landslide stage division based on convolution supporting vector neural network according to claim 3, feature exist
In, convolution supporting vector neural network structure include input layer, convolutional layer, active coating, down-sampling layer, normalization layer and support to
Measure layer.
8. a kind of landslide stage division based on convolution supporting vector neural network according to claim 7, feature exist
In input layer is the transverse and longitudinal coordinate and color of image of image.
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