CN111340021A - Unsupervised domain adaptive target detection method based on center alignment and relationship significance - Google Patents

Unsupervised domain adaptive target detection method based on center alignment and relationship significance Download PDF

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CN111340021A
CN111340021A CN202010104273.6A CN202010104273A CN111340021A CN 111340021 A CN111340021 A CN 111340021A CN 202010104273 A CN202010104273 A CN 202010104273A CN 111340021 A CN111340021 A CN 111340021A
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张勇东
张天柱
吴泽远
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Abstract

The invention discloses an unsupervised domain adaptive target detection method based on center alignment and relationship significance.A corresponding target area proposal is generated for images of a source domain and a target domain through a detector in a training stage; carrying out relation modeling on the target area proposal and the category center, and updating the category center and the target area proposal; the distance between each class of the target domain and the source domain is drawn by using the updated class center, so that the distance between different classes of the target domain is drawn by means of the source domain information; and after training is finished, directly carrying out classification detection on the target domain images. The method does not need to separately calculate the class center, but the class center and the target area proposal are put into the graph to be updated together, so that the model can be trained end to end; when the class centers are aligned, the difference between the classes of the target domain can be enlarged while the distribution difference between the source domain and the target domain is reduced, so that the target domain can be effectively classified.

Description

Unsupervised domain adaptive target detection method based on center alignment and relationship significance
Technical Field
The invention relates to the technical field of target detection, in particular to an unsupervised domain adaptive target detection method based on center alignment and relationship significance.
Background
Target detection is taken as a basic problem of computer vision direction, rapid progress is achieved in recent years under the promotion of deep learning, however, the target detection faces a serious problem, when the distribution of test data is different from that of training data, the detection performance is seriously reduced, the problem is called domain deviation, a data domain used for training a model is called a source domain, a test data domain is called a target domain, one way to solve the problem is to collect data of the target domain, label the data, and then train the data based on the target domain, however, manual data labeling consumes a lot of manpower and material resources, and particularly, the task of labeling positions is needed for the target detection.
In recent years, a method called unsupervised domain adaptation appears in the field of image recognition, and the main idea is to perform distribution matching of a source domain and a target domain. The method is also applied to the field of target detection, but the current method basically follows the experience of image recognition, does not fully consider the characteristics of target detection, and is specifically represented as follows: (1) considering objects in an image individually fails to take full advantage of the relationship of multiple objects in the image. (2) The adopted unsupervised domain adaptation method is often aligned from the angle of the whole domain, and the classification effect is poor due to the fact that the class information of the target domain data cannot be considered in a detailed mode.
Disclosure of Invention
The invention aims to provide an unsupervised domain adaptive target detection method based on center alignment and relationship significance, which can be effectively used for target detection.
The purpose of the invention is realized by the following technical scheme:
an unsupervised domain adaptive target detection method based on center alignment and relationship significance comprises the following steps:
a training stage, generating a corresponding target area proposal for the images of a source domain and a target domain through a detector, wherein the target area proposal refers to a characteristic extracted from an area where a target possibly exists; carrying out relation modeling on the target area proposal and the category center, and updating the category center and the target area proposal; the distance between each class of the target domain and the source domain is drawn by using the updated class center, so that the distance between different classes of the target domain is drawn by means of the source domain information;
after training, classification detection is directly carried out on the target domain images, namely, a target region proposal of the target domain images is firstly generated, then relational modeling is carried out on the target region proposal and the category center of the target domain images, and a target detection result is obtained through a final classifier and a regressor on the target region proposal obtained through final updating.
According to the technical scheme provided by the invention, the class center does not need to be calculated independently, but the class center and the target area proposal are put into the graph to be updated together, so that the model can be trained end to end; when the class centers are aligned, the difference between the classes of the target domain can be enlarged while the distribution difference between the source domain and the target domain is reduced, so that the target domain can be effectively classified.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of an unsupervised domain adaptive target detection method based on center alignment and relationship significance according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are 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 only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides an unsupervised domain adaptive target detection method based on center alignment and relationship significance. The scheme provided by the invention can be applied to the fields of automatic driving and video monitoring, and effectively improves the detection performance in the face of different scene conditions such as weather, illumination, visual angle and the like. In practice, the system can be seamlessly connected with the existing detection system, and hardly brings extra storage and calculation overhead.
Before introducing the method, a dataform is first defined. Source domain data (x)S,yS)∈DS,xS∈XS,yS∈YS，DSData distribution, X, representing the source domainSRepresenting the source domain sample space, YSRepresenting source domain label space, target domain data xT∈XTThe target domain distribution is marked as DTThe target propofol (region proposal) is designated as P ∈ RNxDThe class center of each class of objects is marked as C ∈ RKxDWherein, the target propofol may have the extracted features of the target region, N is the number of the target propofol, D is the dimension of the target propofol, where each target propofol after region pooling is taken as an input of the module, and K is the number of categories. P and C are present for both the source domain and the target domain, hereinafter distinguished by the subscripts S and T.
As shown in fig. 1, which is a flowchart of an unsupervised domain adaptive target detection method based on center alignment and relationship significance provided by the present invention, in the training phase, a corresponding target area proposal is generated for images of a source domain and a target domain by a detector; carrying out relation modeling on the target area proposal and the category center, and updating the category center and the target area proposal; the distance between each class of the target domain and the source domain is drawn by using the updated class center, so that the distance between different classes of the target domain is drawn by means of the source domain information; after training, classification detection is directly carried out on the target domain images, namely, a target region proposal of the target domain images is firstly generated, then relational modeling is carried out on the target region proposal and the category center of the target domain images, and a target detection result is obtained through a final classifier and a regressor on the target region proposal obtained through final updating.
The method is described in detail below.
Our network is based on the two-stage detector, FasterR-CNN, i.e. first extracts the target area and then classifies and positionally adjusts the target area. We add a classifier for domain alignment, a relational significance module for relational modeling, and a category center alignment module for category alignment based on fast R-CNN.
First, a domain alignment mechanism.
For images of a source domain and a target domain, feature maps are extracted through feature extractors respectively, then the feature maps are sent to a region proposal network and a region pooling module to generate corresponding target region proposals (proposal), the target region proposals can be features extracted from regions where targets can exist, in a normal target detector, the region proposals are sent to a final classifier and a regressor for classification and positioning, in the network, the region proposals are used for subsequent relation significance modeling besides the final classification and positioning, wherein the region proposal network is used for generating regions where targets can exist, and the region pooling module pools the features in each region to enable the regions with different sizes to have the same dimension features finally. Meanwhile, we operate on the feature map (located before the area proposal network) to achieve alignment at the whole image domain level, i.e. a domain alignment mechanism, the purpose of domain alignment is to enable the detector to adapt to the target domain image, and of course, the loss function of domain alignment will update the feature extractor, the details are as follows:
in the embodiment of the invention, a countermeasure discriminator is adopted to discriminate the feature maps of a source domain and a target domain; for pictures from a source domain and a target domain respectively, generating a feature map by a feature extractor respectively, and then performing secondary classification by a discriminator to generate a countermeasure loss, wherein a loss function is as follows:
Figure BDA0002387965590000041
where x is the input image, G represents the feature extractor, D represents the discriminator,
Figure BDA0002387965590000042
represents the compound operation of G and D; xS,XTRepresenting source and target domain sample spaces, respectively, DSData distribution representing source domain, DTData distribution of the target domain; e represents expectation.
Second, relation significance modeling
For a source domain and a target domain, respectively putting the generated target region proposal together with each class center to construct a graph network G (V, E), wherein V represents a node set formed by the target region proposal and the class center, E represents an edge set between the nodes, the class center is initialized to a vector of all 0 and is iteratively updated by using the graph network, and an adjacency matrix A ∈ R is used(N+K)x(N+K)Representing the relation among the nodes, and transmitting the information among the nodes in the graph network, thereby generating a category center by using a target area proposal, and simultaneously finely adjusting the characteristics of the target area proposal by the category center; for adjacency matrix A, WW is adoptedTThe calculation is performed, where W is the classification output of the classifier on the node set V, for the class center, the class output is performed without adopting the result of the classifier, but one-hot vectors are directly defined as the class output of the corresponding class center, that is, for the nth class center, the class output is a K-dimensional vector, the vector is 1 in the nth dimension, and the remaining dimensions are all 0, which is to prevent the mutual interference caused by the generation of the result during dot product between the class centers. After the matrix A is calculated, the diagonal line of A is set to 0, and the purpose of setting is to eliminate itself when the adjacent matrix is multiplied by the node. Then, the characteristics of each node and other nodes are aggregated by matrix multiplication, and the source domain and the target domain are calculated in parallel:
PS,CS＝AS*VS
PT,CT＝AT*VT
wherein P, C denotes the target area proposal and the category center, respectively, the subscript S denotes the source domain, and the subscript T denotes the target domain.
In the embodiment of the invention, the result of matrix multiplication is not directly used as a final result, but the calculated node and the original node feature are subjected to weighted summation so as to avoid excessively damaging the original feature; the correlation formula is:
Figure BDA0002387965590000043
Figure BDA0002387965590000044
wherein t and t-1 both represent iteration times, α and theta both represent updated coefficients,
Figure BDA0002387965590000045
respectively obtaining source domain target area proposal after the t iteration, source domain target area proposal generated by an area proposal network during the t iteration and updated information calculated by a graph network during the t iteration;
Figure BDA0002387965590000051
respectively obtaining target domain target area proposals after the t-th iteration, target domain target area proposals generated by an area proposal network during the t-th iteration, and updated information calculated by a graph network during the t-th iteration;
Figure BDA0002387965590000052
respectively the source domain category center after the t iteration, the source domain category center after the t-1 iteration, and the pass
Figure BDA0002387965590000053
GeneratingThe category center of (1);
Figure BDA0002387965590000054
respectively the target domain class center after the t-th iteration and the target domain class center after the t-1 th iteration
Figure BDA0002387965590000055
The generated category centers.
Figure BDA0002387965590000056
Usually a weighted sum of nodes other than the current node, and the weight is the fractional similarity of the current node to the other nodes.
From the above update formula, it can be known that the target propofol is recalculated every iteration, the result of the last iteration is not retained, the class center is global and is initialized to 0, and we respectively maintain the global class centers for the source domain and the target domain as the iteration gets better.
And thirdly, aligning the centers of the categories.
After the class centers are obtained, the class centers can be used for shortening the distance between each class of the target domain and the source domain, so that the different classes of the target domain are separated by the aid of the source domain information, and the different classes of the target domain can be better distinguished.
The operations herein produce a semantic loss, expressed as:
Figure BDA0002387965590000057
wherein, YSRepresenting the source domain label space, K represents the number of categories,
Figure BDA0002387965590000058
respectively representing the category centers of the K type of the source domain and the target domain;
Figure BDA0002387965590000059
is a metric function (e.g., a two-norm distance).
Combining the countermeasure loss, the semantic loss and the detection loss of the source domain, training the whole network together, wherein the total loss function is as follows:
L＝Ldet(XS,YS)+γLda(XS,XT)+λLsm(XS,YS,XT)
where γ, λ are equilibrium coefficients.
Ldet(XS,YS) The detection loss of the source domain consists of two parts, namely classification loss and position regression loss generated by a domain proposal network, and classification loss and regression loss generated by feeding the target domain proposal of the source domain image into a final classifier and a final regressor.
In the embodiment of the present invention, the last classifier and the regressor are disposed at the end of the whole model, where the classifier and the preceding classifier for outputting the area proposal score are the same classifier, and in the training stage, the input of the last classifier and the regressor is the result of the class center alignment module in fig. 1, the last classifier and the regressor are not shown in fig. 1, but the detection loss in fig. 1 actually covers the last classifier and the regressor, and the implementation manner of the two can refer to the prior art, and thus, the description is omitted.
In the testing stage, class center alignment is not required, so that the target area proposal obtained by updating the relation significance module is directly input to the final classifier and the regressor, and a target detection result can be obtained.
According to the scheme of the embodiment of the invention, the class center does not need to be calculated independently, but the class center and the target proposal are put into the graph to be updated together, so that the model can be trained end to end; according to the method, information is shared among features in a graph convolution mode, so that context relationship information is better considered; the method can enlarge the difference between the classes of the target domain while reducing the distribution difference between the source domain and the target domain through the constraint of the class center, thereby effectively classifying the target domain; the method is based on the master R-CNN, no network parameter is required to be added, and no burden is brought to storage and calculation speed. After the whole network training is finished, only a target domain picture is needed to be input, a target region proposal is extracted, the target region proposal is updated by utilizing the relational modeling part and is sent to the final classifier and the regressor, and the two classifiers (for domain alignment) and the class center alignment are not needed in the stage.
Through the above description of the embodiments, it is clear to those skilled in the art that the above embodiments can be implemented by software, and can also be implemented by software plus a necessary general hardware platform. With this understanding, the technical solutions of the embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments of the present invention.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (5)

1. An unsupervised domain adaptive target detection method based on center alignment and relationship significance is characterized by comprising the following steps:
a training stage, generating a corresponding target area proposal for the images of a source domain and a target domain through a detector, wherein the target area proposal refers to a characteristic extracted from an area where a target possibly exists; carrying out relation modeling on the target area proposal and the category center, and updating the category center and the target area proposal; the distance between each class of the target domain and the source domain is drawn by using the updated class center, so that the distance between different classes of the target domain is drawn by means of the source domain information;
after training, classification detection is directly carried out on the target domain images, namely, a target region proposal of the target domain images is firstly generated, then relational modeling is carried out on the target region proposal and the category center of the target domain images, and a target detection result is obtained through a final classifier and a regressor on the target region proposal obtained through final updating.
2. The unsupervised domain-adaptive target detection method based on center alignment and relationship significance as claimed in claim 1, wherein the generating corresponding target region proposals for the images of the source domain and the target domain by the detector comprises:
generating corresponding target region proposals for the images of the source region and the target region respectively through a feature extractor, a region proposal network and a region pooling module; the region pooling module is used for pooling the features in the regions generated by the region proposal network so that the regions with different sizes finally have the features with the same dimension.
3. The unsupervised domain-adaptive target detection method based on center alignment and relational significance according to claim 1, wherein performing relational modeling on the target area proposal and the category center comprises:
for a source domain and a target domain, respectively putting the generated target region proposal together with each category center to construct a graph G (V, E), wherein V represents a node set formed by the target region proposal and the category center, E represents an edge set between nodes, and the category center is initialized to be a vector of 0;
the adjacency matrix A is used for representing the relation between the nodes, and the information between the nodes can be transmitted in the graph, so that a category center is generated by using the target area proposal, and the category center can finely adjust the characteristics of the target area proposal; for adjacency matrix A, WW is adoptedTWhere W is the classification output of the classifier on the node set V, and for the class center, directly defining one-hot vector as the class output of the corresponding class center, that is, for the nth class center, the class output is K-dimensionalSetting the diagonal line of the adjacent matrix A to be 0 after the adjacent matrix A is calculated; then, the characteristics of each node and other nodes are aggregated by matrix multiplication, and the source domain and the target domain are calculated in parallel:
PS,CS＝AS*VS
PT,CT＝AT*VT
wherein P, C denotes the target area proposal and the category center, respectively, the subscript S denotes the source domain, and the subscript T denotes the target domain.
4. The unsupervised domain adaptive target detection method based on center alignment and relationship significance as claimed in claim 3, wherein the formula for updating the class center and target area proposal is as follows:
Figure FDA0002387965580000021
Figure FDA0002387965580000022
wherein α and theta both represent updated coefficients,
Figure FDA0002387965580000023
respectively obtaining source domain target area proposal after the t iteration, source domain target area proposal generated by an area proposal network during the t iteration and updated information calculated by a graph network during the t iteration;
Figure FDA0002387965580000024
respectively obtaining target domain target area proposals after the t-th iteration, target domain target area proposals generated by an area proposal network during the t-th iteration, and updated information calculated by a graph network during the t-th iteration;
Figure FDA0002387965580000025
respectively the source domain category center after the t iteration, the source domain category center after the t-1 iteration, and the pass
Figure FDA0002387965580000026
A generated category center;
Figure FDA0002387965580000027
respectively the target domain class center after the t-th iteration and the target domain class center after the t-1 th iteration
Figure FDA0002387965580000028
The generated category centers.
5. The unsupervised domain-adaptive target detection method based on center alignment and relationship significance as claimed in claim 2, characterized in that the method further introduces a domain alignment mechanism to achieve alignment of image domain level; adopting a countermeasure discriminator to discriminate the feature maps of the source domain and the target domain; for pictures from a source domain and a target domain respectively, generating a feature map by a feature extractor respectively, and then performing secondary classification by a discriminator to generate a countermeasure loss, wherein a loss function is as follows:
Figure FDA0002387965580000029
where x is the input image, G represents the feature extractor, D represents the discriminator,
Figure FDA00023879655800000210
represents the compound operation of G and D; xS,XTRepresenting source and target domain sample spaces, respectively, DSData distribution representing source domain, DTData distribution of the target domain; e represents expectation;
when the updated class center is used to zoom in the distance between the target domain and the source domain, semantic loss is generated and is expressed as:
Figure FDA00023879655800000211
wherein, YSRepresenting the source domain label space, K represents the number of categories,
Figure FDA00023879655800000212
respectively representing the category centers of the K type of the source domain and the target domain;
Figure FDA00023879655800000213
is a metric function;
the total loss function for the training phase is:
L＝Ldet(XS,YS)+γLda(XS,XT)+λLsm(XS,YS,XT)
wherein γ, λ are equilibrium coefficients; l isdet(XS,YS) The detection loss of the source domain consists of two parts, namely classification loss and position regression loss generated by a domain proposal network, and classification loss and regression loss generated by feeding the target domain proposal of the source domain image into a final classifier and a final regressor.
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