CN112819762A - Pavement crack detection method based on pseudo-twin dense connection attention mechanism - Google Patents

Pavement crack detection method based on pseudo-twin dense connection attention mechanism Download PDF

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CN112819762A
CN112819762A CN202110087473.XA CN202110087473A CN112819762A CN 112819762 A CN112819762 A CN 112819762A CN 202110087473 A CN202110087473 A CN 202110087473A CN 112819762 A CN112819762 A CN 112819762A
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王彩玲
陈良全
蒋国平
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Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a pavement crack detection method based on a pseudo-twin dense connection attention mechanism, which comprises the following steps of: step S1, acquiring a data set; step S2, preprocessing the pictures in the training set; step S3, constructing a pseudo-twin residual error network; step S4, designing a loss function of a pseudo-twin residual error network, training the pseudo-twin residual error network until the loss function is converged, and storing a model; and step S5, detecting cracks of the picture under test by using the model obtained in the step S4. According to the invention, by improving the traditional Encoder-Decoder model, the detection result under a mixed background, namely a mixed data set, can be effectively detected; and the loss function is optimized, so that the method is more suitable for the pavement crack background.

Description

Pavement crack detection method based on pseudo-twin dense connection attention mechanism
Technical Field
The invention relates to the field of image segmentation, in particular to a pavement crack detection method based on a pseudo-twin dense connection attention mechanism.
Background
With the increase of the road surface area year by year, the manpower and the energy for detecting and maintaining the road surface also increase year by year, and the artificial detection of the road surface cracks not only has certain errors but also increases the danger of detection personnel when the detection is carried out on the road surface, so that the design of an automatic road surface crack detector is necessary. The purpose of automatic pavement crack detection is to output a detection result by inputting a pavement picture or a video sequence, although the traditional method can also realize automatic detection, the efficiency and the detection precision of the traditional method have defects all the time, most of research works focus on a pavement crack detection algorithm based on a deep learning method at present, and a deep learning model can obtain the segmentation or prediction of a crack distribution region through the training of a large number of data sets. In most cases, the distribution of the pavement cracks is unbalanced, for example, some cracks are distributed more finely, some cracks are thicker, some cracks are distributed in complex texture, and some cracks are very simple, so that the detector which needs to be designed can adapt to the distribution of most pavement cracks.
In road maintenance, it is necessary to use an automatic detection device based on deep learning, which can improve not only detection efficiency but also detection accuracy. The detection capability of the deep learning model requires a large number of data sets to train, and there are now a large number of open-source and heterogeneous road surface crack data sets from the open-source code project hosting platform GitHub. Relatively few studies are currently conducted on detection models for mixed fractures.
Disclosure of Invention
In view of the above, the present invention provides a road surface crack detection method based on a pseudo-twin dense connection attention mechanism. The method improves the traditional Encoder-Decoder model, so that the detection result under the mixed background, namely the mixed data set can be effectively detected, and the loss function is optimized, so that the method is more suitable for the pavement crack background.
In order to achieve the purpose, the invention provides the following technical scheme:
a pavement crack detection method based on a pseudo-twin dense connection attention mechanism is characterized by comprising the following steps:
step S1, acquiring a data set, and dividing the data set into a training set and a test set;
step S2, preprocessing the pictures in the training set;
step S3, constructing a pseudo-twin residual error network;
s4, designing a loss function of a pseudo-twin residual error network, wherein the loss function is obtained by weighting a focus loss function and an L1 regular loss, training the pseudo-twin residual error network until the loss function is converged, and storing a model;
and step S5, detecting cracks of the picture under test by using the model obtained in the step S4.
Further, the data set includes: crack500, Crack200, CFD, AEL and GAPs 384.
Further, in step S1, the acquiring the data set specifically includes:
searching an open-source database on a GitHub platform, wherein the search keywords are as follows: version, crack and detection; the project languages of the database are Python and C + +; the ordering is labeled most star.
Further, the step S2 includes: the pictures are divided into three types of coarse cracks, fine cracks and uniform cracks according to the distribution size of the cracks in the pictures, and the pictures are processed to be 480 x 320 in resolution.
Further, the step S3 includes: the pseudo-twin residual error network takes SegNet as a basic framework, and specifically comprises the following steps: the encoder network and the decoder network have the same structure, the structure of the encoder network is five convolution layers which are connected in sequence, the size of the first convolution layer is 3 x 64, the step length is 2, the size of the second convolution layer is 3 x 128, the step length is 2, the size of the third convolution layer is 3 x 256, the step length is 2, the size of the fourth convolution layer is 3 x 512, the step length is 2, the size of the fifth convolution layer is 3 x 512, and the step length is 2; the Padding method of the five-layer convolutional layer is Valid.
Furthermore, an attention mechanism network is added on the basis of the basic frame, the attention mechanism network adopts five convolutional layers, and the attention mechanism network and the encoder network adopt pseudo-twin input to form a pseudo-twin network;
the attention mechanism network is used for generating an attention parameter, and the encoder network is used for extracting crack features;
the attention mechanism network is densely connected with the encoder network, each layer in the attention mechanism network generates an attention parameter, and the attention parameters weight each layer in the encoder network in a densely connected mode;
adding a residual block into the decoder on the basis of the basic framework, wherein the expression of the l-th layer residual block is as follows:
Figure BDA0002911413110000021
in formula (1), ReslRepresented as a residual block of the l-th layer,
Figure BDA0002911413110000022
expressed as a characteristic of the l +1 layer in the encoder,
Figure BDA0002911413110000023
expressed as the characteristics of the l +1 layer in the decoder.
Further, the expression of the attention parameter is as follows:
α＝P(σ2(Fl)) (2)
in the formula (2), σ2Expressed as sigmoid activation function, P denotes resampling operation, FlIs shown in equation (3):
Figure BDA0002911413110000024
in the formula (3), the first and second groups,
Figure BDA0002911413110000031
to representIs the input of the l layers,
Figure BDA0002911413110000032
expressed as hyper-parameters for performing 1 x 1 convolution operations,
Figure BDA0002911413110000033
expressing the attention parameter characteristic equation, the expression is shown as formula (4):
Figure BDA0002911413110000034
in the formula (4), Wi TAnd
Figure BDA0002911413110000035
respectively expressed as the weight and offset of the l layers, BNγ,βDenotes batch normalization processing, σ1Indicating the Relu activation function.
Further, the expression of the loss function is:
loss(T,P)＝α*Tversky(T,P)+(1-α)L1(T,P) (5)
in equation (5), T is expressed as a true segmentation map, P is expressed as a predicted segmentation map, α is 0.7, L1 represents L1 regular loss, and Tversky represents a Tversky loss function.
The invention has the beneficial effects that:
according to the invention, by improving the traditional Encoder-Decoder model, the detection result under a mixed background, namely a mixed data set, can be effectively detected; and the loss function is optimized, so that the method is more suitable for the pavement crack background.
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Fig. 1 is a flowchart of a road surface crack detection method provided in embodiment 1.
Fig. 2 is a schematic structural diagram of the pseudo-twin residual network provided in embodiment 1.
Fig. 3 is a flowchart of attention parameter generation in embodiment 1.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1 to 3, the present embodiment provides a pavement crack detection method based on a pseudo-twin dense connection attention mechanism, including the following steps:
step S1, acquiring a data set, and dividing the data set into a training set and a test set;
specifically, in this implementation, an open source database is first searched on GitHub.
The search keyword is set to "vector credit detection", and for the selection of an item, the present embodiment selects an item (Python, C + +) marked in 2 languages as a keyword, and the sort mark is "most star".
Five public data sets were finally collected, respectively: crack500, Crack200, CFD, AEL and GAPs 384.
Step S2, preprocessing the pictures in the training set;
in particular, because each data set has its own characteristics, the data set needs to be preprocessed before use. Where Crack500 and Crack200 belong to the coarse fracture dataset, GAPs384 belong to the fine fracture dataset, and CFD and AEL belong to the homogeneous dataset. In addition, the picture size of each data set is different, and the present embodiment performs resize operation on all pictures through the PIL library in Python, all of which are set to 480 × 320 pixels.
Finally, this embodiment eliminates some of the marked inaccurate data and integrates them together in ascending order of numbers. For the training set and the test set, the present embodiment sets the ratio of training and test data to 9: 1.
step S3, constructing a pseudo-twin residual error network;
specifically, the pseudo-twin residual error network takes SegNet as a basic framework, and specifically comprises the following steps: the method comprises the steps of carrying out symmetrical encoder-decoder model structure, wherein an encoder and a decoder both have the same structure and are symmetrically connected, the encoder adopts convolution and pooling operation, the decoder adopts reverse convolution and reverse pooling operation, the encoder characteristics are restored layer by layer, and a two-value segmentation graph with the number of output channels of the last layer of the decoder being 1, namely a crack prediction result graph.
The structure of the encoder is five convolution layers which are connected in sequence, the size of the first convolution layer is 3 x 64, the step size is 2, the size of the second convolution layer is 3 x 128, the step size is 2, the size of the third convolution layer is 3 x 256, the step size is 2, the size of the fourth convolution layer is 3 x 512, the step size is 2, the size of the fifth convolution layer is 3 x 512, and the step size is 2; the Padding method for five-layer convolutional layers is Valid.
In this embodiment, on the basis of the basic framework, an attention mechanism network is added, which specifically includes:
the embodiment adopts a strategy of a pseudo-twin network, the twin network needs all directions to share the weight, and the network structure designed by the embodiment does not need to share the network weight. The pseudo-twin residual error network provided by this embodiment has two input ends, and the input ends are the same RGB fracture picture, where the encoder network is a main network and is used for extracting fracture features, the attention generating network is an auxiliary network and is mainly used for generating attention parameters, and the attention parameters of each layer not only act on the auxiliary current layer, but also serve as an auxiliary for feature extraction of all layers below.
With the convolution and pooling operations, the size of the feature map is continuously reduced, the number of channels is continuously increased, and meanwhile, the detail information of the feature map is lost layer by layer.
Wherein the attention parameter α is obtained by the following formula:
Figure BDA0002911413110000041
Figure BDA0002911413110000042
α＝P(σ2(Fl)) (3)
in the formula (1) to the formula (3),
Figure BDA0002911413110000051
is an input to the l layer, Wi TAnd
Figure BDA0002911413110000052
is the weight and deviation of the l layer, sigma 2 refers to sigmoid activation function, P is resampling, so that the size of the current attention parameter conforms to the characteristic size of the current coding layer characteristic, otherwise, weighting operation cannot be carried out,
Figure BDA0002911413110000053
hyper-parameters for performing 1 x 1 convolution operations.
If the order of magnitude of the feature is too large, it will enter its saturation region early when passing the activation function, so the batch normalization BN is usedγ,βAnd σ 1 is the Relu activation function,
Figure BDA0002911413110000054
is to note the parametric characteristic equation. FlIs a feature of the transformed attention parameter.
Finally, the attention parameters are obtained by Softmax and max-posing, wherein the Sigmoid activation function is defined as follows:
Figure BDA0002911413110000055
in the attention mechanism network introduced by the embodiment, the parameters generated by each layer are not only used in the current layer, but all the layers are multiplexed later, and then the input feature graph F epsilon R of the encoderH*W*CH × W is the size of the feature map, i.e. 480 × 320 is initially set, C is the number of channels of the feature map, and RGB three channels are initially set; feature map F 'after each layer'
Figure BDA0002911413110000056
Multiplexing the attention parameters of each layer also requires row rolling and pooling the feature matrix to fit the size of the current layer features. The final encoder portion forms a pseudo-twin intensive attention mechanism.
Through double input, the auxiliary channel guides the generation of the main channel characteristics, and attention parameters are multiplexed, so that the loss of detail information in the process of convolution and pooling of the crack characteristics can be reduced, namely in the generated crack detection picture, the edge information of the crack appears smooth and lacks texture details.
Besides, the present embodiment introduces a residual block in the decoder, and the generating process of the l-th layer residual value is as follows:
Figure BDA0002911413110000057
in equation (5), ReslRepresented as a residual block of the l-th layer,
Figure BDA0002911413110000058
expressed as the characteristics of the l +1 layer in the encoder,
Figure BDA0002911413110000059
expressed as the characteristics of the l +1 layer in the decoder.
Due to the asymmetry of the network, the encoder layer characteristics need to be rolled and inverse pooled according to the characteristics of the decoder layer, and finally the decoder characteristics of the l-th layer are generated by the decoder characteristics of the l + 1-th layer and the residual block together.
More specifically, in this embodiment, the SegNet is selected as the basic framework, which is determined through experiments, specifically as follows:
in this embodiment, for a pseudo-twin residual error network to be constructed, three basic frames are selected in advance, which are fusasionnet, Unet and SegNet respectively; obtaining data through a design experiment, and finally selecting SegNet as a basic frame through data comparison; the method comprises the following specific steps:
since the selection of the base framework does not involve the content of the design, only three sets of comparative experiments were designed on the CFD data set, and the experimental results are shown in table 1:
table 1: dice index of fusion Net, UNet and SegNet on CFD dataset
Figure BDA0002911413110000061
It can be seen from the table that SegNet as a base frame is more suitable for pavement crack backgrounds. Meanwhile, in this experiment, it is found that the detection accuracy of the detector in the table is general, and the detailed information of the crack is seriously lost, even the crack is broken between cracks, so the purpose of this embodiment is to grasp the detailed information of the crack while improving the detection accuracy.
Then, a SegNet network is used for testing three types of data sets, the test index is the MIoU index, and the experimental result is shown in table 2:
table 2: testing of SegNet on three types of data sets
Figure BDA0002911413110000062
S4, designing a loss function of the pseudo-twin residual error network, wherein the loss function is obtained by weighting a focus loss function and an L1 regular loss, training the pseudo-twin residual error network until the loss function is converged, and storing a model;
in particular, the selection of the loss function plays a certain decisive role in the learning of the network. The traditional pavement crack detection network generally selects a cross entropy loss function, but the cross entropy loss function has great limitation on a data set with unbalanced data distribution, and the background of pavement crack data is generally noisy, so that the background can be detected as cracks by adopting the cross entropy loss function. That is, during network training, background features, such as low-contrast shaded portions, are continuously enlarged in the convolutional layer, and finally the detector is caused to segment the background as well as the crack features.
Therefore, in the embodiment, in addition to adopting the Tversky loss function instead of the cross-entropy loss function, the L1 regular loss is also adopted to enhance the robustness of the network. Tverseky loss is defined as the generalized coefficients of the Dice coefficient and the Jaccard coefficient:
Figure BDA0002911413110000063
in equation (6), the Tversky loss is the Dice coefficient when α ═ β is 0.5, and the Tversky loss is the Jaccard coefficient when α ═ β is 1. We set α -1- β -0.7. The overall loss function is a weighting of the Tversky loss and the L1 regularization loss, defined as:
loss(T,P)＝α*Tversky(T,P)+(1-α)L1(T,P) (7)
in equation (7), T is the true segmentation map, P is the predicted segmentation map, and the loss coefficient is set to α equal to 0.7.
And step S5, detecting the cracks of the picture in the test by using the model obtained in the step S4.
In this example, to verify the performance of the method, a comparative experiment was also performed, and the experimental results are shown in table 3.
Table 3: experimental results of comparative algorithm
Figure BDA0002911413110000071
Through the experiments, the method provided by the embodiment is superior to other detectors in precision, recall ratio and F1-measure, and the superiority of the pseudo-twin residual error network is shown.
The invention has the advantages of reducing the problems of crack detail information loss and crack fracture existing in the detection result of the current crack detector, thereby improving the detection precision.
The invention is not described in detail, but is well known to those skilled in the art.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (8)

1. A pavement crack detection method based on a pseudo-twin dense connection attention mechanism is characterized by comprising the following steps: step S1, acquiring a data set, and dividing the data set into a training set and a test set;
step S2, preprocessing the pictures in the training set;
step S3, constructing a pseudo-twin residual error network;
s4, designing a loss function of a pseudo-twin residual error network, wherein the loss function is obtained by weighting a focus loss function and an L1 regular loss, training the pseudo-twin residual error network until the loss function is converged, and storing a model;
and step S5, detecting cracks of the picture under test by using the model obtained in the step S4.
2. The method of claim 1, wherein the data set comprises: crack500, Crack200, CFD, AEL and GAPs 384.
3. The method for detecting the road surface crack based on the pseudo-twin dense connection attention mechanism as claimed in claim 1, wherein in the step S1, the acquiring the data set specifically comprises:
searching an open-source database on a GitHub platform, wherein the search keywords are as follows: version, crack and detection; the project languages of the database are Python and C + +; the ordering is labeled most star.
4. A pavement crack detection method based on a pseudo-twin dense joint attention mechanism according to any one of claims 1-3, wherein the step S2 includes: the pictures are divided into three types of coarse cracks, fine cracks and uniform cracks according to the distribution size of the cracks in the pictures, and the pictures are processed to be 480 x 320 in resolution.
5. The method for detecting the road surface crack based on the pseudo-twin dense connection attention mechanism as claimed in claim 4, wherein the step S3 includes: the pseudo-twin residual error network takes SegNet as a basic framework, and specifically comprises the following steps: the encoder network and the decoder network have the same structure, the structure of the encoder network is five convolution layers which are connected in sequence, the size of the first convolution layer is 3 x 64, the step length is 2, the size of the second convolution layer is 3 x 128, the step length is 2, the size of the third convolution layer is 3 x 256, the step length is 2, the size of the fourth convolution layer is 3 x 512, the step length is 2, the size of the fifth convolution layer is 3 x 512, and the step length is 2; the Padding method of the five-layer convolutional layer is Valid.
6. The pavement crack detection method based on the pseudo-twin dense connection attention mechanism is characterized in that an attention mechanism network is added on the basis of the basic frame, five convolutional layers are adopted in the attention mechanism network, and the attention mechanism network and the encoder network adopt pseudo-twin input to form the pseudo-twin network;
the attention mechanism network is used for generating an attention parameter, and the encoder network is used for extracting crack features;
the attention mechanism network is densely connected with the encoder network, each layer in the attention mechanism network generates an attention parameter, and the attention parameters weight each layer in the encoder network in a densely connected mode;
adding a residual block into the decoder on the basis of the basic framework, wherein the expression of the l-th layer residual block is as follows:
Figure FDA0002911413100000021
in formula (1), ReslRepresented as a residual block of the l-th layer,
Figure FDA0002911413100000022
expressed as a characteristic of the l +1 layer in the encoder,
Figure FDA0002911413100000023
expressed as the characteristics of the l +1 layer in the decoder.
7. The method for detecting the pavement crack based on the pseudo-twin dense connection attention mechanism as claimed in claim 6, wherein the expression of the attention parameter is as follows:
α＝P(σ2(Fl)) (2)
in the formula (2), σ2Expressed as sigmoid activation function, P denotes resampling operation, FlIs shown in equation (3):
Figure FDA0002911413100000024
in the formula (3), the first and second groups,
Figure FDA0002911413100000025
the input is represented as a layer/of,
Figure FDA0002911413100000026
expressed as hyper-parameters for performing 1 x 1 convolution operations,
Figure FDA0002911413100000027
expressing the attention parameter characteristic equation, the expression is shown as formula (4):
Figure FDA0002911413100000028
in the formula (4), Wi TAnd
Figure FDA0002911413100000029
respectively expressed as the weight and offset of the l layers, BNγ,βDenotes batch normalization processing, σ1Indicating the Relu activation function.
8. The method for detecting the pavement crack based on the pseudo-twin dense connection attention mechanism as claimed in claim 7, wherein the expression of the loss function is as follows:
loss(T,P)＝α*Tversky(T,P)+(1-α)L1(T,P) (5)
in equation (5), T is expressed as a true segmentation map, P is expressed as a predicted segmentation map, α is 0.7, L1 represents L1 regular loss, and Tversky represents a Tversky loss function.
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