KR102476679B1 - Apparatus and method for object detection - Google Patents

Abstract

An embodiment of the present invention provides an object detection apparatus using a similarity between categories and an eigenvector of each of the categories. The present object detection apparatus includes an input module and a processor that analyzes data input to the input module. The processor generates a similarity graph generated to indicate a degree of similarity between the first categories based on sentence data for the first categories, and 1 corresponding to the first categories through a convolution neural network (CNN). An eigenvector of each of the first categories is set using feature information of objects extracted from the primary image, and the similarity graph is calculated using sentence data for a second category different from the first categories. The similarity graph is modified to indicate the degree of similarity between categories and categories including the secondary category, and through the feature information of objects extracted from the primary image, the modified similarity graph, and the CNN, the secondary An eigenvector of the secondary category is set using feature information of objects extracted from the secondary image corresponding to the category.

Description

Object detection device and method {APPARATUS AND METHOD FOR OBJECT DETECTION}

The present invention relates to an object detection apparatus and method, and more particularly, to deep learning using a similarity graph and an eigenvector generated based on a correlation between categories, using only a small number of training data for an object corresponding to a new category. It relates to an object detection device and method enabling object detection based on the present invention.

Object detection is a key element of image processing technology that identifies the location and type of an object in an image. Recently, object detection technology is rapidly developing through big data and deep learning. Deep learning-based object detection technology enables more accurate detection of the location and type of various objects in an image through a large amount of learning data.

For object detection with high accuracy, a large number of training data with pre-labeled correct answers are required, but in comparison, the amount of data in most industries and academic fields is still insufficient. Since it takes a lot of time and money for people to identify the types and locations of objects in images in various fields, it is difficult to practically use deep learning-based object detection technology. In addition, in order to learn a new type of object in an actual service environment, it is practically difficult to additionally secure a large amount of learning data for a corresponding object.

Accordingly, research on a fractional shot learning technique has recently been attracting attention. The prime-shot learning technique aims to correctly predict an object in an image having a corresponding category when a model trained with a large number of training data is given a small number of training data having a new unlearned category. Conventional small-shot learning techniques have been studied limited to image classification techniques that predict the type of an object in an image. Meanwhile, constructing training data for object detection incurs a relatively higher cost than constructing training data for object classification. Accordingly, interest in object detection technology based on a small number of shots is growing.

The present invention is intended to solve the above-described problem, using a similarity graph and eigenvector generated based on correlation between categories, deep learning-based object detection is possible with only a small amount of training data for objects corresponding to a new category. It is a technical task to provide an object detection device and method that enables this.

The technical problems to be achieved by the present invention are not limited to the above technical problems, and other technical problems of the present invention can be derived from the following description.

As a technical means for achieving the above-described technical problem, an object detection apparatus using a similarity between categories and an eigenvector of each of the categories is provided according to an aspect of the present invention. The present object detection apparatus includes an input module and a processor that analyzes data input to the input module. The processor generates a similarity graph generated to indicate a degree of similarity between the first categories based on sentence data for the first categories, and 1 corresponding to the first categories through a convolution neural network (CNN). An eigenvector of each of the first categories is set using feature information of objects extracted from the primary image, and the similarity graph is calculated using sentence data for a second category different from the first categories. The similarity graph is modified to indicate the degree of similarity between categories and categories including the secondary category, and through the feature information of objects extracted from the primary image, the modified similarity graph, and the CNN, the secondary An eigenvector of the secondary category is set using feature information of objects extracted from the secondary image corresponding to the category.

In addition, according to another aspect of the present invention, an object detection method using a similarity between categories and an eigenvector of each of the categories is provided. The present object detection method includes a similarity graph generated to indicate the degree of similarity between the first categories based on sentence data for the first categories, and a convolutional neural network (CNN) that corresponds to the first categories. Setting an eigenvector of each of the first categories using feature information of objects extracted from the first image; and constructing the similarity graph using sentence data for a second category different from the first categories. Modifying the similarity graph to indicate a degree of similarity between categories including the first categories and the second category, the feature information of objects extracted from the first image, the modified similarity graph, and the CNN. and setting an eigenvector of the second category by using feature information of objects extracted from the second image corresponding to the second category through

According to the above-described problem solving means of the present invention, highly accurate deep learning-based object detection is possible with only a small number of training data for objects corresponding to a new category.

In addition, according to the present invention, high-speed machine learning can be performed in a field where learning data for object detection is extremely small, such as a robot field.

In addition, according to the present invention, an existing object detection model can be applied to a new service environment by additionally applying only a small number of new learning data to an existing object detection model learned with a large number of data.

The effects of the present invention are not limited to the effects described above, and include all effects understood from the following description.

1 is a block diagram showing the configuration of an object detection apparatus according to an embodiment of the present invention.
2 to 4 are diagrams for explaining an object detection method using the object detection apparatus shown in FIG. 1 .
5 is a flowchart illustrating a sequence of an object detection method according to another embodiment of the present invention.
6 and 7 are flowcharts illustrating detailed processes of some steps of the object detection method shown in FIG. 5 .

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings. However, the present invention may be implemented in many different forms, and is not limited to the embodiments described herein. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in this specification, and the technical ideas disclosed in this specification are not limited by the accompanying drawings. In order to clearly explain the present invention in the drawings, parts irrelevant to the description are omitted, and the size, shape, and shape of each component shown in the drawings may be variously modified. Same/similar reference numerals are assigned to the same/similar parts throughout the specification.

The suffixes "module" and "unit" for components used in the following description are given or used interchangeably in consideration of ease of writing the specification, and do not have meanings or roles that are distinct from each other by themselves. In addition, in describing the embodiments disclosed in this specification, if it is determined that a detailed description of related known technologies may obscure the gist of the embodiments disclosed in this specification, the detailed description is omitted.

Throughout the specification, when a part is said to be “connected (connected, contacted, or combined)” with another part, this is not only the case where it is “directly connected (connected, contacted, or coupled)”, but also has other members in the middle. It also includes the case of being "indirectly connected (connected, contacted, or coupled)" between them. In addition, when a part "includes (provides or provides)" a certain component, it does not exclude other components, but "includes (provides or provides)" other components unless otherwise specified. means you can

Terms indicating ordinal numbers such as first and second used in this specification are used only for the purpose of distinguishing one element from another, and do not limit the order or relationship of elements. For example, a first element of the present invention may be termed a second element, and similarly, the second element may also be termed a first element.

A “device” referred to below may be implemented as a computer or portable terminal capable of accessing a server or other terminals through a network. Here, the computer includes, for example, a laptop, desktop, laptop, etc. equipped with a web browser, and the portable terminal is, for example, a wireless communication device that ensures portability and mobility. , IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), Wibro (Wireless Broadband Internet), LTE (Long Term Evolution) communication-based terminal, smart All types of handheld-based wireless communication devices such as phones and tablet PCs may be included. In addition, the network may include a wired network such as a Local Area Network (LAN), a Wide Area Network (WAN) or a Value Added Network (VAN), a mobile radio communication network, a satellite communication network, and the like. It can be implemented in all kinds of wireless networks such as

1 is a block diagram showing the configuration of an object detection device 100 according to an embodiment of the present invention, and FIGS. 2 to 4 are diagrams for explaining object detection using the object detection device 100 . Hereinafter, the object detection apparatus 100 will be described in detail with reference to FIGS. 1 to 4 .

Referring to FIG. 1 , the object detection apparatus 100 includes an input module 110 and a processor 120, and may further include a memory 130. Here, the input module 110 may include a device including hardware and software necessary for transmitting/receiving a signal such as a control signal or a data signal through a wired/wireless connection with another network device. The processor 120 may include various types of devices that control and process data. The processor 120 may refer to a data processing device embedded in hardware having a physically structured circuit to perform functions expressed by codes or instructions included in a program. In one example, the processor 120 may include a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, an application-specific integrated circuit (ASIC), an FPGA ( field programmable gate array), etc., but the scope of the present invention is not limited thereto. The memory 130 should be interpreted as collectively referring to a non-volatile storage device that continuously maintains stored information even when power is not supplied and a volatile storage device that requires power to maintain stored information. The memory 130 may temporarily or permanently store data processed by the processor 120 . The memory 130 may include magnetic storage media or flash storage media in addition to volatile storage devices that require power to maintain stored information, but the scope of the present invention is not limited thereto. no.

The input module 110 receives data required for programs and functions executed by the processor 120 . The input module 110 may receive image data corresponding to each of several categories for deep learning and object detection of the processor 120 . Here, the category means the type of object. In one example, the category may be a person, a bicycle, or an airplane, but the scope of the present invention is not limited thereto. Although not shown in the drawing, the input module 110 may include a camera module for capturing an image of an object, a computer input device, a data input circuit, and a communication module.

The processor 120 analyzes data input to the input module 110 . The processor 120 uses a similarity graph generated to indicate a degree of similarity between primary categories and feature information of objects extracted from primary images corresponding to the primary categories through a Convolution Neural Network (CNN), An eigenvector of each of the tea categories may be set. CNN, as a convolutional neural network, should be interpreted as including various artificial neural network models for deep learning-based data learning, object classification, image data analysis, and especially object detection from image data. In one example, the processor 120 may learn to infer a location where an object will exist and its shape and type from image data using a Fully Connected Layer (FCL). In addition, the processor 120 may vectorize the image by reducing the dimensions of the image through global average pooling (GAP), and learn that the corresponding vector represents a unique characteristic of each category.

Characteristic information of objects extracted from the primary image through CNN may include at least one or more of shape information, size information, and contour information of each of the objects extracted from the primary image. In one example, the primary categories may include people, airplanes, bicycles, buses, trains, motorcycles, monitors, TVs, chairs, sofas, cats, tigers, cows, and sheep, but the scope of the present invention is not limited thereto. no. The primary image refers to an image including objects corresponding to each of the primary categories. In one example, the primary image may include a video image of a person, a video image of an airplane, and a video image of a bus.

The similarity graph represents the similarity calculated based on sentence data for the first categories. The similarity graph may be generated by the processor 120 and may be stored in the memory 130 . To generate a similarity graph, the processor 120 first performs word embedding in consideration of co-occurrence frequencies of words included in sentence data for categories. Here, the word embedding may mean optimizing a process of embedding a relationship between words based on a co-occurrence frequency between words in a sentence using global vectors for word representation. Thereafter, the processor 120 calculates the cosine similarity between each word embedding using the performed word embeddings to generate a similarity graph representing the similarity between categories in the form of a matrix or the like.

Referring to FIG. 2 showing an example of a similarity graph, the similarity between a person and an airplane is 0.32, the similarity between a person and a motor bike is 0.52, and a person and a bicycle ( The similarity between bicycles may be set to 0.53, the similarity between person and horse to 0.5, and the similarity between motor bike and bicycle to 0.9. However, the similarity graph according to the present invention may be generated in the form of various graphs capable of indicating names of categories and similarity values between categories, as well as the graph shown in FIG. 2 .

Meanwhile, the first categories may include first to third categories. At this time, if the similarity between the first category and the second category is greater than the similarity between the first category and the third category, the processor 120 determines the difference between the eigenvector set for the first category and the eigenvector set for the second category. may be set to be smaller than the difference between the eigenvector set in the first category and the eigenvector set in the third category. In this regard, reference is made to FIG. 3 showing an example in which eigenvectors set for each of the primary categories are visualized in the form of a graph including an X axis and a Y axis.

As described above with reference to FIG. 2, the similarity (0.9) between a bicycle and a motor bike is higher than the similarity (0.53) between a bicycle and a person. Therefore, in the graph of FIG. 3, the distance between the point corresponding to the bicycle and the point corresponding to the motor bike is closer than the distance between the point corresponding to the bicycle and the point corresponding to the person. You can see what has been set. That is, the size of the difference between the eigenvector of the bicycle and the eigenvector of the motor bike is set to be smaller than the size of the difference between the eigenvector of the bicycle and the eigenvector of the person. In this way, by setting the eigenvector of the category in consideration of the similarity between categories as well as the feature information of the object, it is possible to effectively set the eigenvector for a new category to be described below.

The processor 120 may modify the similarity graph so that the similarity graph represents the degree of similarity between the categories including the first categories and the second categories by using sentence data of the second categories that are different from the first categories. In one example, the processor 120 uses sentence data of an electric kickboard, which is a new category not included in the first category, to create a similarity graph showing only the similarity between the first categories (human, bicycle, motorcycle, airplane) in a second order Can be modified to include categories. The modification process may be performed similarly to the process of generating the above-described similarity graph.

The processor 120 uses the feature information of objects extracted from the primary image, the modified similarity graph, and the feature information of objects extracted from the secondary image corresponding to the secondary category through CNN to determine the second category. Eigenvectors can be set. In one example, when the processor 120 sets the eigenvectors of the 2nd category in a state where the eigenvectors of the 1st categories are set, the eigenvectors can be set with only a small number of images corresponding to the 2nd category.

More specifically, first, the processor 120 may divide the primary image to obtain primary divided images for each location in the primary image. The processor 120 may determine a position where an object exists in the primary image from the primary division images. The processor 120 may identify an object in the primary image and classify the identified object into one of the primary categories. The processor 120 may perform a convolutional product of a first division image having an object among first division images and an eigenvector set for a category of an object in the first division image having an object. Accordingly, convolution results for each of the first categories may be generated. The processor 120 may perform more sophisticated object analysis on images corresponding to each of the first categories by using a convolution result for each of the first categories.

Next, the processor 120 may divide the secondary image to obtain secondary divided images for each location in the secondary image. The processor 120 may determine a position where an object exists in the secondary image from the secondary division images. The processor 120 may perform a convolutional product of a second division image in which an object exists among second division images and an eigenvector set in a second category. Accordingly, a convolution result for the second category may be generated. The processor 120 may perform more sophisticated object analysis from an image corresponding to the second category by using a convolution result for the second category.

Each of the above-described primary image and secondary image includes a plurality of images. The number of images included in the primary image may be set to be greater than the number of images included in the secondary image. In one example, first, the processor 120 may complete data learning on objects classified into the first categories based on thousands of images corresponding to the first categories. Thereafter, the processor 120 may perform data learning on objects classified into the second category using a small number of images corresponding to the second category, for example, only a few images of the second category. In this way, the processor 120 may more accurately extract the locations and types of objects from images classified into the second categories by using preset eigenvectors and similarity graphs for the first categories.

Referring to FIG. 4 showing an example of image data analysis exaggeration of the object detection device 100 . The processor 120 performs deep learning on the first image corresponding to the first categories and the second image corresponding to the second category, and then selects one of categories including the first categories and the second category. An analysis target image 410 corresponding to the category of may be input. The processor 120 generates convolutional results for CNN and each of the first categories. In addition, the object may be identified from the image to be analyzed using the convolution result for the second category. In operation 420, the processor 120 determines the position of the object identified from the analysis target image within the analysis target image, and assigns the object identified from the analysis target image to any one of categories including primary categories and secondary categories. Indicates sorting into categories. Thereafter, an analysis result 430 of finally identifying an object from the analysis target image 410 may be generated.

5 is a flow chart showing a sequence of an object detection method according to another embodiment of the present invention, and FIGS. 6 and 7 are flowcharts showing detailed processes of some steps of the object detection method shown in FIG. 5 . Hereinafter, the object detection method according to the present embodiment will be described in detail with reference to FIGS. 5 to 7 .

The object detection method according to this embodiment may be performed by the object detection device (100 in FIG. 1) described above with reference to FIGS. 1 to 4. For example, the steps described below may be executed by a processor ( 120 in FIG. 1 ) of the object detection device ( 100 in FIG. 1 ). Accordingly, the description of the object detection device (100 in FIG. 1) described above with reference to FIGS. 1 to 4 may be equally applied to the object detection method below.

In the object detection method according to the present embodiment, a similarity graph generated to indicate the degree of similarity between the first categories based on sentence data of the first categories and a convolution neural network (CNN) are used to detect the first categories. Setting an eigenvector of each of the first categories by using feature information of objects extracted from the corresponding primary image (S110), using sentence data for a secondary category different from the primary categories, and a similarity graph Modify the similarity graph to indicate the degree of similarity between the categories including the first categories and the second categories (S120), and through the feature information of objects extracted from the primary image, the modified similarity graph, and CNN, 2 An eigenvector of the second category is set using feature information of objects extracted from the second image corresponding to the second category (S130).

In step S110, the primary image is segmented to obtain primary segmented images for each location in the primary image (S111), and the location of the object in the primary image is determined from the primary segmented images (S112). and identifying an object in the primary image and classifying the identified object into one of primary categories (S113). In addition, in step S110, a convolutional product is performed between the first division image in which the object exists among the first division images and the eigenvector set for the category of the object in the first division image in which the object exists, for each of the first division categories. A step of generating a convolution result (S114) may be further included.

Characteristic information of objects extracted from the primary image may include at least one of shape information, size information, and contour information of each of the objects extracted from the primary image.

The first categories include first to third categories, and in step S110, when the degree of similarity between the first and second categories is greater than the degree of similarity between the first and third categories, the eigenvector set in the first category A difference between eigenvectors set in the second category and eigenvectors set in the second category may be set to be smaller than a difference between eigenvectors set in the first category and eigenvectors set in the third category.

The primary image used in step S110 and the secondary image used in step S130 each include a plurality of images, and the number of images included in the primary image is set to be greater than the number of images included in the secondary image. It can be.

Step S130 is a step of dividing the secondary image to obtain secondary segmented images for each position in the secondary image (S131), determining a location where an object exists in the secondary image from the secondary segmentation images (S132), and A step of generating a convolution result for the second category by performing a convolution between a second division image in which an object exists among second division images and an eigenvector set in the second category (S133).

The object detection method according to the present embodiment includes a step of receiving an analysis target image corresponding to any one of categories including first categories and second categories, which is performed after step S130 (S140), and , CNN, convolution results for each of the first-order categories. The method may further include identifying an object from the image to be analyzed using a convolution result for the second category (S150).

Step S150 is. Determining the location of the object identified from the analysis target image within the analysis target image (S151), and assigning the object identified from the analysis target image to any one of categories including the first categories and the second category. It may include a step of classifying into (S152).

The object detection method described with reference to FIGS. 5 to 7 may be implemented in the form of a recording medium including instructions executable by a computer, such as program modules executed by a computer. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. Also, computer readable media may include computer storage media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Although the methods and systems of the present invention have been described with reference to specific embodiments, some or all of their components or operations may be implemented using a computer system having a general-purpose hardware architecture.

The above description of the present invention is for illustrative purposes, and those skilled in the art can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, the embodiments described above should be understood as illustrative in all respects and not limiting. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form. The scope of the present invention is indicated by the following claims rather than the detailed description above, and all changes or modifications derived from the meaning and scope of the claims and equivalent concepts should be construed as being included in the scope of the present invention. do.

Claims (19)

An object detection apparatus using a similarity between categories and an eigenvector set for each of the categories,
input module; and
A processor for analyzing data input to the input module;
the processor,
A similarity graph generated to indicate the degree of similarity between the first categories based on sentence data for the first categories, and extracted from the first images corresponding to the first categories through a convolution neural network (CNN) Using feature information of objects, setting an eigenvector of each of the first categories;
modifying the similarity graph so that the similarity graph indicates a degree of similarity between categories including the first categories and the second categories using sentence data for a second category different from the first categories;
Using the feature information of objects extracted from the primary image, the modified similarity graph, and the feature information of objects extracted from the secondary image corresponding to the secondary category through the CNN, An object detection device configured to perform setting an eigenvector. According to claim 1,
the processor,
The primary image is segmented to obtain primary segmented images for each position in the primary image, a position where an object exists in the primary image is determined from the primary segmented images, and an object in the primary image is determined. and further performing identifying and classifying the identified object into any one of the primary categories. According to claim 2,
the processor,
A convolution result of each of the first categories is performed by performing a convolution between a first division image in which an object exists among the first division images and an eigenvector set for a category of an object in the first division image in which the object exists. An object detection device configured to further perform generating an object detection device. According to claim 1,
The feature information of the objects extracted from the primary image includes at least one or more of shape information, size information, and contour information of each of the objects extracted from the primary image. According to claim 1,
The first categories include first to third categories,
the processor,
When the degree of similarity between the first category and the second category is greater than the degree of similarity between the first category and the third category, the difference between the eigenvector set in the first category and the eigenvector set in the second category is and setting the difference between the eigenvector set in the first category and the eigenvector set in the third category to be smaller than that of the eigenvector set in the third category. According to claim 1,
The first image and the second image each include a plurality of images, and the number of images included in the first image is set to be greater than the number of images included in the second image. According to claim 3,
the processor,
The secondary image is segmented to obtain secondary segmented images for each location in the secondary image, a location where an object exists in the secondary image is determined from the secondary segmented images, and among the secondary segmented images and performing a convolutional product of a second division image in which an object exists and an eigenvector set in the second category to generate a convolution result for the second category. According to claim 7,
the processor,
Receiving an analysis target image corresponding to any one of categories including the first categories and the second categories;
CNN, convolution results for each of the first-order categories. And, the object detection apparatus configured to further perform identification of an object from the analysis target image by using a convolutional product result for the second category. According to claim 8,
the processor,
The location of an object identified from the analysis target image is determined within the analysis target image, and the object identified from the analysis target image is assigned to any one of categories including the first categories and the second category. An object detection device, configured to further perform classification. An object detection method performed by an object detection apparatus using a similarity between categories and an eigenvector set for each of the categories,
(a) a similarity graph generated by the object detection apparatus to indicate a degree of similarity between the first categories based on sentence data of the first categories, and the first categories through a Convolution Neural Network (CNN) setting an eigenvector of each of the primary categories by using feature information of objects extracted from the primary image corresponding to;
(b) The object detection apparatus causes the similarity graph to indicate a degree of similarity between categories including the first categories and the second categories by using sentence data for a second category different from the first categories. modifying the similarity graph; and
(c) The object detection device, through the feature information of objects extracted from the primary image, the modified similarity graph, and the feature information of objects extracted from the secondary image corresponding to the secondary category through the CNN and setting an eigenvector of the second category by using , an object detection method. According to claim 10,
In step (a),
obtaining, by the object detection device, first divided images for each position in the first image by dividing the first image;
determining, by the object detection device, a position where an object exists in the primary image from the primary division images; and
and classifying, by the object detection device, an object in the primary image and classifying the identified object into one of the primary categories. According to claim 11,
In step (a),
The object detection apparatus performs a convolutional product of a first division image in which an object exists among the first division images and an eigenvector set for a category of an object in the first division image in which the object exists, and determines the first categories Further comprising the step of generating a convolution result for each, the object detection method. According to claim 10,
The feature information of the objects extracted from the primary image includes at least one or more of shape information, size information, and contour information of each of the objects extracted from the primary image. According to claim 10,
The first categories include first to third categories,
In step (a), when the similarity between the first category and the second category is greater than the similarity between the first category and the third category, the eigenvector set to the first category and the second category set The difference between the eigenvectors set to be smaller than the difference between the eigenvectors set in the first category and the eigenvectors set in the third category. According to claim 10,
The primary image used in step (a) and the secondary image used in step (c) each include a plurality of images, and the number of images included in the primary image is included in the secondary image. An object detection method that is set to be greater than the number of images displayed. According to claim 12,
In step (c),
obtaining, by the object detection device, second divided images for each location in the secondary image by dividing the secondary image;
determining, by the object detection device, a location where an object exists in the secondary image from the secondary divided images; and
The object detection device performs a convolutional product of a second division image in which an object exists among the second division images and an eigenvector set in the second category to generate a convolution result for the second category. , an object detection method. According to claim 16,
(d) receiving, by the object detection device, an analysis target image corresponding to any one of categories including the first categories and the second categories; and
The object detection apparatus, (e) CNN, a convolution result for each of the first categories. and identifying an object from the analysis target image by using a convolutional product result for the second category. According to claim 17,
In step (e),
determining, by the object detection device, a position of an object identified from the analysis target image within the analysis target image; and
and classifying, by the object detection device, an object identified from the analysis target image into any one category among categories including the first categories and the second category. A non-transitory computer-readable recording medium on which a computer program for performing the object detection method according to any one of claims 10 to 18 is recorded.

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