CN108288269A - Bridge pad disease automatic identifying method based on unmanned plane and convolutional neural networks - Google Patents
Bridge pad disease automatic identifying method based on unmanned plane and convolutional neural networks Download PDFInfo
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- CN108288269A CN108288269A CN201810066755.XA CN201810066755A CN108288269A CN 108288269 A CN108288269 A CN 108288269A CN 201810066755 A CN201810066755 A CN 201810066755A CN 108288269 A CN108288269 A CN 108288269A
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- 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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
- G01M5/0008—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings of bridges
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
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- 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
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- 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
- G06T2207/20084—Artificial neural networks [ANN]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30132—Masonry; Concrete
Abstract
The present invention provides a kind of bridge pad disease automatic identifying method based on unmanned plane and convolutional neural networks, includes the following steps：Bridge pad disease photo is obtained, with the method for image procossing, increases the data volume for training convolutional neural networks；The bridge pad disease photo of acquisition is divided into training set and test set；Convolutional neural networks are established, and pass through the weights of gradient descent method and each layer of back-propagation algorithm repetitive exercise convolutional neural networks；Obtain the convolutional neural networks model with automatic identification bridge pad disease function；Ground control system control unmanned plane cruises, and the photo of bridge pad is obtained using the image capture device that unmanned plane carries；The data of unmanned plane acquisition are passed to high in the clouds and carry out data processing, trained convolutional neural networks model carries out the automatic identification of bridge pad disease for use.Invention has the advantages of high efficiency, at low cost, has apparent advantage compared to the artificial detection method of traditional bridge pad disease.
Description
Technical field
It is specifically a kind of based on unmanned plane and convolution god the present invention relates to civil engineering and artificial intelligence interaction technique field
Bridge pad disease automatic identifying method through network.
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.With the fast development of unmanned air vehicle technique, the application of unmanned plane is also more
Penetrate into all trades and professions, wherein unmanned multi-rotor aerocraft can be used for the disease of bridge because of simple in structure, relative low price
Evil detection.
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 unmanned plane and convolution
The bridge pad disease automatic identifying method of neural network.
Technical solution：In order to solve the above technical problems, a kind of bridge based on unmanned plane and convolutional neural networks of the present invention
Beam support disease 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：Convolutional neural networks, including input and output layer, convolutional layer and pond layer are established, input is located in advance by step S3
The image of reason, and pass through the weights of gradient descent method and each layer of back-propagation algorithm repetitive exercise convolutional neural networks；Had
There is the convolutional neural networks model of automatic identification bridge pad disease function；
S5：Ground control system control unmanned plane cruises, and is obtained using the image capture device that unmanned plane carries
The photo of bridge pad；
S6：The data of unmanned plane acquisition are passed to high in the clouds and carry out data processing, use trained convolutional Neural net
Network model carries out the automatic identification of bridge pad disease.
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, convolutional neural networks are the VGG16 network structures of classics, and in the network, first layer is input layer,
It is Softmax output layers to receive the coloured image that pixel size is 224 × 224 and be used as input, last layer, altogether N number of node, N
Indicate 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.
In step S4, 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 S4, 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.
In step S5, unmanned plane is unmanned multi-rotor aerocraft, and the image capture device carried is high-definition camera, is used
In obtaining clearly bridge pad photo.
In step S6, cloud processor uses the AWS cloud computing services of Amazon.
Advantageous effect：A kind of bridge pad disease automatic identification side based on unmanned plane and convolutional neural networks of the present invention
Method has the advantages that：
It is efficient, it is at low cost, there is apparent advantage compared to the artificial detection method of traditional bridge pad disease.
Description of the drawings
Fig. 1 is that the present invention uses unmanned plane and the bridge pad disease automatic identification based on unmanned plane and convolutional neural networks
The flow chart of method；
Fig. 2 is the VGG16 schematic network structures used 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 unmanned plane and convolutional neural networks, including
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：Convolutional neural networks, including input and output layer, convolutional layer and pond layer are established, input is located in advance by step S3
The image of reason, and pass through the weights of gradient descent method and each layer of back-propagation algorithm repetitive exercise convolutional neural networks；Had
There is the convolutional neural networks model of automatic identification bridge pad disease function；
S5：Ground control system control unmanned plane cruises, and is obtained using the image capture device that unmanned plane carries
The photo of bridge pad；
S6：The data of unmanned plane acquisition are passed to high in the clouds and carry out data processing, use trained convolutional Neural net
Network model carries out the automatic identification of bridge pad disease.
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, convolutional neural networks are the VGG16 network structures of classics, as shown in Fig. 2, in the network, first
Layer is input layer, and it is Softmax output layers to receive the coloured image that pixel size is 224 × 224 and be used as input, last layer,
N number of node altogether, N indicate 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.
In step S4, 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 S4, 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.
In step S5, unmanned plane is unmanned multi-rotor aerocraft, and the image capture device carried is high-definition camera, is used
In obtaining clearly bridge pad photo.
In step S6, cloud processor uses the AWS cloud computing services of Amazon.
The experiment condition of training convolutional neural networks：Make Amazon AWS cloud computing services, configures Amazon
EC2P2.xlarge examples, 1 GPU of the exemplary configuration, 4 vCPU, the random access memory of 61GB, system is using ubuntu
System, programming language use python.Deep learning platform uses Theano.
The database of the data set of training convolutional neural networks, bridge pad disease is color image data library, including but
Unlimited and common bridge pad disease photo, shear-deformable, bearing compression external drum of bearing season cracking, bearing etc..
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 unmanned plane and convolutional neural networks, it is characterised in that：Including
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：Convolutional neural networks, including input and output layer, convolutional layer and pond layer are established, input is pretreated by step S3
Image, and pass through the weights of gradient descent method and each layer of back-propagation algorithm repetitive exercise convolutional neural networks；It obtains having certainly
The convolutional neural networks model of dynamic identification bridge pad disease function；
S5：Ground control system control unmanned plane cruises, and obtains bridge using the image capture device that unmanned plane carries
The photo of bearing；
S6：The data of unmanned plane acquisition are passed to high in the clouds and carry out data processing, use trained convolutional neural networks mould
Type carries out the automatic identification of bridge pad disease.
2. the bridge pad disease automatic identifying method according to claim 1 based on unmanned plane and convolutional neural networks,
It is characterized in that：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 unmanned plane and convolutional neural networks,
It is characterized in that：In step S2, the amount of images for training convolutional neural networks is increased by the method for image procossing, is used for
Improve the generalization ability of neural network.
4. the bridge pad disease automatic identifying method according to claim 1 based on unmanned plane and convolutional neural networks,
It is characterized in that：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 unmanned plane and convolutional neural networks,
It is characterized in that：In step S3, the preprocess method of image is：Then divided by image the sum of pixel value of all images is calculated
Quantity obtains a mean value image, and the pixel value of the mean value image is subtracted in every piece image.
6. the bridge pad disease automatic identifying method according to claim 1 based on unmanned plane and convolutional neural networks,
It is characterized in that：In step S4, convolutional neural networks are the VGG16 network structures of classics, and in the network, first layer is input
Layer, it is Softmax output layers to receive the coloured image that pixel size is 224 × 224 and be used as input, last layer, altogether N number of section
Point, N indicate 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.
7. the bridge pad disease automatic identifying method according to claim 1 based on unmanned plane and convolutional neural networks,
It is characterized in that：In step S4, gradient descent method the specific steps are：The gradient of each weights of counting loss function pair, from appoint
A little starting, the negative direction along the gradient moves a distance, continues along gradient reverse direction operation a distance in new position,
The weights of network are constantly updated in this way.
8. the bridge pad disease automatic identifying method according to claim 1 based on unmanned plane and convolutional neural networks,
It is characterized in that：In step S4, back-propagation algorithm the specific steps are：Convolutional Neural 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.
9. the bridge pad disease automatic identifying method according to claim 1 based on unmanned plane and convolutional neural networks,
It is characterized in that：In step S5, unmanned plane is unmanned multi-rotor aerocraft, and the image capture device carried is high-definition camera
Head, for obtaining clearly bridge pad photo.
10. the bridge pad disease automatic identifying method according to claim 1 based on unmanned plane and convolutional neural networks,
It is characterized in that：In step S6, cloud processor uses the AWS cloud computing services of Amazon.
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Cited By (9)
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| CN109606678A (en) * | 2018-11-22 | 2019-04-12 | 东南大学 | A kind of crawler-type unmanned machine being automatically positioned bridge pad |
| CN109859184A (en) * | 2019-01-29 | 2019-06-07 | 牛旗 | A kind of real-time detection of continuous scanning breast ultrasound image and Decision fusion method |
| CN110070008A (en) * | 2019-04-04 | 2019-07-30 | 中设设计集团股份有限公司 | Bridge disease identification method adopting unmanned aerial vehicle image |
| CN110222701A (en) * | 2019-06-11 | 2019-09-10 | 北京新桥技术发展有限公司 | A kind of bridge defect automatic identifying method |
| CN111562220A (en) * | 2020-06-02 | 2020-08-21 | 吉林大学 | Rapid and intelligent detection method for bridge diseases |
| WO2020199538A1 (en) * | 2019-04-04 | 2020-10-08 | 中设设计集团股份有限公司 | Bridge key component disease early-warning system and method based on image monitoring data |
| CN112326686A (en) * | 2020-11-02 | 2021-02-05 | 坝道工程医院(平舆) | Unmanned aerial vehicle intelligent cruise pavement disease detection method, unmanned aerial vehicle and detection system |
| 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 |
| CN112884760A (en) * | 2021-03-17 | 2021-06-01 | 东南大学 | Near-water bridge multi-type disease intelligent detection method and unmanned ship equipment |
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|---|---|---|---|---|
| CN109606678A (en) * | 2018-11-22 | 2019-04-12 | 东南大学 | A kind of crawler-type unmanned machine being automatically positioned bridge pad |
| CN109606678B (en) * | 2018-11-22 | 2021-09-07 | 东南大学 | Crawler-type unmanned aerial vehicle capable of automatically positioning bridge support |
| CN109859184A (en) * | 2019-01-29 | 2019-06-07 | 牛旗 | A kind of real-time detection of continuous scanning breast ultrasound image and Decision fusion method |
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| CN110070008A (en) * | 2019-04-04 | 2019-07-30 | 中设设计集团股份有限公司 | Bridge disease identification method adopting unmanned aerial vehicle image |
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| CN110222701A (en) * | 2019-06-11 | 2019-09-10 | 北京新桥技术发展有限公司 | A kind of bridge defect automatic identifying method |
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| CN111562220A (en) * | 2020-06-02 | 2020-08-21 | 吉林大学 | Rapid and intelligent detection method for bridge diseases |
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| CN112326686B (en) * | 2020-11-02 | 2024-02-02 | 坝道工程医院(平舆) | Unmanned aerial vehicle intelligent cruising pavement disease detection method, unmanned aerial vehicle and detection system |
| 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 |
| CN112884760A (en) * | 2021-03-17 | 2021-06-01 | 东南大学 | Near-water bridge multi-type disease intelligent detection method and unmanned ship equipment |
| WO2022193420A1 (en) * | 2021-03-17 | 2022-09-22 | 东南大学 | Intelligent detection method for multiple types of diseases of bridge near water, and unmanned surface vessel device |
| CN112884760B (en) * | 2021-03-17 | 2023-09-26 | 东南大学 | Intelligent detection method for multi-type diseases of near-water bridge and unmanned ship equipment |
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Application publication date: 20180717 |