CN110826588A - Drainage pipeline defect detection method based on attention mechanism - Google Patents

Drainage pipeline defect detection method based on attention mechanism Download PDF

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CN110826588A
CN110826588A CN201910810653.9A CN201910810653A CN110826588A CN 110826588 A CN110826588 A CN 110826588A CN 201910810653 A CN201910810653 A CN 201910810653A CN 110826588 A CN110826588 A CN 110826588A
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潘刚
穆罕默德·乌马尔·泽山
郑耀先
孙迪
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Abstract

The invention relates to a drainage pipeline defect detection method based on an attention mechanism, which comprises the following steps: extracting a defect picture from the pipeline defect detection report, classifying the defect picture according to the defect type corresponding to the defect picture, and totally dividing into: deformation, corrosion, scaling, stagger, deposition, leakage, and cracking; constructing an attention mechanism module, wherein the attention mechanism module is a CBAM module; building a neural network model based on an attention mechanism, wherein the last 3 convolutional layers of the VGG16 network are removed from the convolutional network, and a plurality of CBAM modules are added on the basis; training the neural network by using a back propagation algorithm, verifying in the training process, and storing an optimal model for network training when the verification accuracy is highest; and testing the test set by using the stored optimal model to obtain a classification result of the pipeline defect picture.

Description

Drainage pipeline defect detection method based on attention mechanism
Technical Field
The invention relates to the computer field of computer vision, computer image processing, deep learning and the like and the drainage pipeline abnormity detection field. The invention more focuses on the application of the deep learning technology to the drainage pipeline defect detection.
Background
Drainage pipe systems are one of the largest infrastructures in cities intended to collect and transport wastewater and rain water. The normal use of the system is very important for the safety of urban drainage. With the rapid development of cities in recent years, China highlights the problems of insufficient underground pipeline construction scale, low management level and the like. Some cities have rainstorm, road collapse and other accidents in succession, and the life and urban operation order of people are seriously influenced. Regular inspection and repair of drainage pipelines is therefore an indispensable measure in urban construction.
Pipeline Closed Circuit Television (CCTV) is the most widely used technology in the pipeline Inspection field to date. The system consists of a pipeline detection robot and a CCTV camera arranged on the robot, and the equipment acquires video data (1) containing the internal information of the pipeline under the remote control operation of staff or the automatic control of a computer. And the inspector performs manual defect identification according to the captured video and then writes a pipeline defect detection report. This approach is often overly dependent on the experience of the test person, is highly subjective, and requires a great deal of time and effort.
In recent years, deep learning, particularly Convolutional Neural Networks (CNNs), has made tremendous progress in the field of computer vision. A convolutional neural network-based pipeline defect detection method is then generated, for example: kumar et al, 2018, proposed a simple classification convolutional neural network for classifying three defects, root invasion, deposition and fissures [2 ]; li et al use a deep convolutional neural network with hierarchical classification to detect classification of sewer defect types from unbalanced CCTV inspection data detection [3 ]. However, these methods do not utilize the unique features of the pipeline data, and the effect is not ideal.
Reference to the literature
[1]Costello，S.，Chapman，D.，Rogers，C.，Metje，N.：Underground assetlocation and condition assessment technologies.Tunnelling and UndergroundSpace Technology 22(5-6)，524-542(2007).
[2]Kumar，Srinath S.，et al.″Automated defect classification in sewerclosed circuit television inspections using deep convolutional neuralnetworks.″Automation in Construction 91(2018)：273-283.
[3]Li，Duanshun，Anran Cong，and Shuai Guo.″Sewer damage detection fromimbalanced CCTV inspection data using deep convolutional neural networks withhierarchica1 classification.″Automation in Construction 101(2019)：199-208.
Disclosure of Invention
The invention uses a structure based on a VGG-16 convolutional neural network as a main model, provides an automatic detection method for the pipeline defects based on an attention mechanism to solve the problems, and utilizes a large number of labeled pipeline abnormal samples to train and test. The technical scheme of the invention is as follows:
a drainage pipeline defect detection method based on an attention mechanism comprises the following steps of:
step 1: extracting a defect picture from the pipeline defect detection report, classifying the defect picture according to the defect type corresponding to the defect picture, and totally dividing into: deformation, corrosion, scaling, stagger, deposition, leakage, and cracking.
Step 2: the data set is divided into a training set test set and a validation set.
And step 3: and constructing an Attention mechanism Module, wherein the adopted Attention mechanism Module is a CBAM (ConvolationBlock Attention Module), and the Module can be directly added into a convolutional neural network model and used as a network layer. The input of the module is the output O e R of the convolution layerC×H×WWherein C is the channel number of the Feature map, H and W are the height and width of the Feature map respectively, and O passes through the channel attention module A successivelyC∈RC×1×1And spatial attention Module AS∈R1×H×WFinally obtaining output Oa∈RC×H×W.
Figure BDA0002184955120000021
Figure BDA0002184955120000022
Wherein
Figure BDA0002184955120000023
Representing element-by-element multiplication, O1Intermediate results are shown. O obtained via CBAM ModuleaCan be directly re-fed into other parts of the convolutional neural network.
And 4, step 4: building a neural network model based on an attention mechanism, wherein the last 3 convolutional layers of the VGG16 network are removed from the convolutional network, and a plurality of CBAM modules are added on the basis, and the general structure of the convolutional neural network is as follows:
the first convolution layer has a convolution kernel size of 3 × 3, 64 filters in total, and a stride of 1.
The first attention module, CBAM module, scaling factor r 8.
The second convolution layer has a convolution kernel size of 3 × 3, 64 filters in total, and a stride of 1.
The first largest pooling layer, pooling size 2 x 2, stride 2.
In the third convolution layer, the convolution kernel size is 3 × 3, 128 filters in total, and stride is 1.
The second attention module, CBAM module, scales by a factor r of 8.
The fourth convolution layer has a convolution kernel size of 3 × 3, 128 filters in total, and a stride of 1.
The second largest pooling layer, pooling size 2 x 2, stride 2.
In the fifth convolution layer, the convolution kernel size is 3 × 3, 256 filters in total, and stride is 1.
The third attention module, CBAM module, scales by a factor r of 8.
In the sixth convolution layer, the convolution kernel size is 3 × 3, 256 filters in total, and stride 1.
The fourth attention module, CBAM module, scales by a factor r of 8.
The seventh convolution layer has a convolution kernel size of 3 × 3, a total of 256 filters, and a stride of 1.
The third largest pooling layer, pooling size 2 x 2, stride 2.
In the eighth convolution layer, the convolution kernel size is 3 × 3, and there are 512 filters in total, and stride is 1.
The fifth attention module, CBAM module, scales by a factor r of 8.
In the ninth convolution layer, the convolution kernel size is 3 × 3, 512 filters in total, and stride is 1.
The sixth attention module, CBAM module, scales by a factor r of 8.
In the tenth convolution layer, the convolution kernel size is 3 × 3, a total of 512 filters, and the stride is 1.
The fourth largest pooling layer, pooling size 2 x 2, stride 2.
In the first full link layer, the number of nodes is 4096.
In the second full connection layer, the number of nodes is 4096.
And finally, outputting the probability that the image corresponds to 7 abnormal categories by a Softmax layer.
And 5: and training the neural network by using a back propagation algorithm, verifying in the training process, and storing the optimal model of the network training when the verification accuracy is highest.
Step 6: and testing the test set by using the stored optimal model to obtain a classification result of the pipeline defect picture.
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FIG. 1 is a flow chart of the present invention
FIG. 2 is a network architecture diagram of the present invention
FIG. 3 is a structural diagram of CBAM
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail below with reference to the accompanying drawings by way of examples. It is clear that the described implementations are only a part of the embodiments of the invention, and not an exhaustive list of all embodiments. And features of the implementations and embodiments of the present description may be combined with each other without conflict.
The processing steps of the invention comprise: preparing and processing data, establishing a training set verification set test set, training a convolutional neural network, and automatically classifying defects by adopting a trained network model.
Step 1: and (4) preparing and processing data. And extracting a defect picture inside the pipeline from the obtained drainage pipeline defect detection report by adopting a python program, and classifying the picture according to the report content and the defect type. Selecting seven types of pictures with good picture quality, such as deformation, corrosion, scaling, dislocation, deposition, leakage, cracking and the like.
Step 2: and dividing seven types of selected deformation, corrosion, scaling, stagger, deposition, leakage and other defect types into a training set, a verification set and a test set respectively, wherein the division ratio is 3: 1.
And step 3: and (4) building an attention mechanism module, wherein the adopted attention mechanism structure is a CBAM module. The input of the module is the output O e R of the convolution layerC×H×WWhere C is the number of channels of the Feature map, and H and W are the height and width of the Feature map, respectively. The attention mechanism module can be divided into 2 parts, wherein the first part is a channel attention module, and the second part is a space attention module.
The first part passes the input O through the channel attention module aC(O)∈RC×1×1To obtain an intermediate output O1∈RC×H×W。
The part can be divided into the following 3 steps:
(1) firstly, respectively carrying out global average pooling GloAvgPool and global maximum pooling GloMaxPool on input, and then respectively passing the result through two full-connection layers Dense1And Dense2To obtain two outputs d with the same dimension1∈RC×1×1And d2∈RC×1×1Namely:
d1＝Dense2(Dense1(GloAvgPool(O)))
d2＝Dense2(Dense1(GloMaxPool(O)))
wherein Dense1The number of the nodes is
Figure BDA0002184955120000041
r represents a scaling factor, and in this experiment r is 8. Dense2The number of the nodes is C.
(2) Will d1And d2Adding, sending the result into sigmoid function to obtain channel attention weight AC(O)∈RC×1×1. The calculation formula is as follows:
AC(O)＝Sigmoid(d1+d2)
(3) input O and channel attention weight AC(O)∈RC×1×1Performing element-by-element multiplication to obtain the output O of the first part1∈RC×H×W。
Figure BDA0002184955120000042
Representing element-by-element multiplication, the calculation formula is as follows:
Figure BDA0002184955120000043
intermediate result O to be obtained by the second part1Through space attention Module AS(O1)∈R1×H×WFinally obtaining output Oa∈RC×H×W. The part can be divided into the following 4 steps:
(1) the inputs were averaged in channel dimensions ChanAvgPool and maximal pooled ChanMaxPool, respectively, and the results were then concatenated. The calculation formula is as follows:
dc＝[ChanAvgPool(O1)；ChanMaxPool(O1)]
(2) the result d obtainedcPassing through a convolutional layer having a filter with a size of 7 × 7, and then feeding the output of the convolutional layer into a sigmoid function to obtain a spatial attention weight AS(O1)∈R1×H×W。
AS(O1)＝Sigmoid(Con2D7×7(dc))
(3) Intermediate result O1And spatial attention weight AS(O1)∈R1×H×WPerforming element-by-element multiplication to obtain output Oa∈RC×H×W. The calculation formula is as follows:
Figure BDA0002184955120000044
and 4, step 4: and (3) building a convolutional neural network model based on an attention mechanism, wherein the last 3 convolutional layers of the VGG16 network are removed from the convolutional network, and a plurality of CBAM modules are added on the basis. The model overall network structure is as follows:
the first convolution layer has a convolution kernel size of 3 × 3, 64 filters in total, and a stride of 1.
The first attention module, CBAM module, scaling factor r 8.
The second convolution layer has a convolution kernel size of 3 × 3, 64 filters in total, and a stride of 1.
The first largest pooling layer, pooling size 2 x 2, stride 2.
In the third convolution layer, the convolution kernel size is 3 × 3, 128 filters in total, and stride is 1.
The second attention module, CBAM module, scales by a factor r of 8.
The fourth convolution layer has a convolution kernel size of 3 × 3, 128 filters in total, and a stride of 1.
The second largest pooling layer, pooling size 2 x 2, stride 2.
In the fifth convolution layer, the convolution kernel size is 3 × 3, 256 filters in total, and stride is 1.
The third attention module, CBAM module, scales by a factor r of 8.
In the sixth convolution layer, the convolution kernel size is 3 × 3, 256 filters in total, and stride 1.
The fourth attention module, CBAM module, scales by a factor r of 8.
The seventh convolution layer has a convolution kernel size of 3 × 3, a total of 256 filters, and a stride of 1.
The third largest pooling layer, pooling size 2 x 2, stride 2.
In the eighth convolution layer, the convolution kernel size is 3 × 3, and there are 512 filters in total, and stride is 1.
The fifth attention module, CBAM module, scales by a factor r of 8.
In the ninth convolution layer, the convolution kernel size is 3 × 3, 512 filters in total, and stride is 1.
The sixth attention module, CBAM module, scales by a factor r of 8.
In the tenth convolution layer, the convolution kernel size is 3 × 3, a total of 512 filters, and the stride is 1.
The fourth largest pooling layer, pooling size 2 x 2, stride 2.
In the first full link layer, the number of nodes is 4096.
In the second full connection layer, the number of nodes is 4096.
And finally, outputting the probability that the image corresponds to 7 abnormal categories by a Softmax layer.
And 5: and training the convolutional neural network model, and storing the optimal model weight.
Step 6: and automatically classifying the pipeline defect pictures by adopting the stored optimal model weight.
The network structure is built by taking TensorFlow as a background and using a Keras deep learning library. The language used was Python. In this embodiment, a convolutional neural network structure and internal operations such as a convolutional layer, a max pooling layer, a full link layer, a Softmax layer, an Add function, and a Multiply function are constructed by writing convolutional network layers in the library through a function model in the Keras deep learning library as an overall structure.
According to the pipeline abnormal type detection method provided by the embodiment of the invention, after the image to be recognized is obtained, the classification can be carried out without manually defining the characteristics by a user, and the type of the image to be recognized can be judged by directly utilizing the deep learning network obtained by pre-training. And because the attention mechanism module is added in the network, the network can gradually extract the unique characteristics of different defect pictures in the training process, so that the recognition capability of the network on the pipeline defect pictures is greatly improved, the automatic detection and classification of the pipeline defects can be accurately realized, and the network has good application value.

Claims (3)

1. A drainage pipeline defect detection method based on an attention mechanism comprises the following steps:
step 1: extracting a defect picture from the pipeline defect detection report, classifying the defect picture according to the defect type corresponding to the defect picture, and totally dividing into: deformation, corrosion, scaling, stagger, deposition, leakage, and cracking.
Step 2: dividing a data set into a training set test set and a verification set;
and step 3: constructing an Attention mechanism Module, wherein the adopted Attention mechanism Module is a CBAM (ConvolationBlock Attention Module), and the Module can be directly added into a convolutional neural network model and used as a network layer; the input of the module is the output O e R of the convolution layerC×H×WWherein C is the channel number of the Feature map, H and W are the height and width of the Feature map respectively, and O passes through the channel attention module A successivelyC∈RC×1×1And spatial attention Module AS∈R1×H×WFinally obtaining output Oa∈RC×H×W.
Figure FDA0002184955110000011
Figure FDA0002184955110000012
Wherein
Figure FDA0002184955110000013
Representing element-by-element multiplication, O1Representing an intermediate result; o obtained via CBAM ModuleaCan be directly re-fed into other parts of the convolutional neural network;
and 4, step 4: building a neural network model based on an attention mechanism, wherein the last 3 convolutional layers of the VGG16 network are removed from the convolutional network, and a plurality of CBAM modules are added on the basis, and the general structure of the convolutional neural network is as follows:
a first convolution layer with convolution kernel size of 3 × 3, 64 filters in total, and stride of 1;
a first attention module, CBAM module, scaling factor r 8;
a second convolution layer with a convolution kernel size of 3 × 3, 64 filters in total, and a stride of 1;
a first maximum pooling layer with a pooling size of 2 x 2 and a stride of 2;
a third convolution layer, the convolution kernel size is 3 × 3, 128 filters in total, and stride is 1;
a second attention module, CBAM module, scaling factor r 8;
a fourth convolution layer, with a convolution kernel size of 3 × 3, 128 filters in total, and a stride of 1;
a second maximum pooling layer with a pooling size of 2 x 2 and a stride of 2;
a fifth convolution layer with a convolution kernel size of 3 × 3, a total of 256 filters, and a stride of 1;
a third attention module, CBAM module, scaling factor r 8;
a sixth convolution layer with a convolution kernel size of 3 × 3, a total of 256 filters, and a stride of 1;
a fourth attention module, CBAM module, scaling factor r 8;
a seventh convolution layer, with a convolution kernel size of 3 × 3, a total of 256 filters, and a stride of 1;
a third maximum pooling layer with a pooling size of 2 x 2 and a stride of 2;
an eighth convolution layer, the convolution kernel size is 3 × 3, 512 filters in total, and stride is 1;
a fifth attention module, CBAM module, scaling factor r 8;
a ninth convolution layer, with a convolution kernel size of 3 × 3, a total of 512 filters, and a stride of 1;
a sixth attention module, CBAM module, scaling factor r 8;
a tenth convolution layer with a convolution kernel size of 3 × 3, a total of 512 filters, and a stride of 1;
a fourth maximum pooling layer, the pooling size being 2 x 2, and the stride being 2;
4096 nodes in the first full-connection layer;
in the second full-connection layer, the number of nodes is 4096;
a Softmax layer, which finally outputs the probability that the image corresponds to 7 abnormal categories;
and 5: training the neural network by using a back propagation algorithm, verifying in the training process, and storing an optimal model for network training when the verification accuracy is highest;
step 6: and testing the test set by using the stored optimal model to obtain a classification result of the pipeline defect picture.
2. The method according to claim 1, wherein in step 2), the data enhancement method is selected from gaussian noise, image inversion and color dithering.
3. The method of claim 1, wherein in step 5), network training is performed using an Nvidia GPU.
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Application publication date: 20200221