Image detection method and apparatus, electronic device, and storage medium

Abstract

Provided are an image detection method and apparatus, an electronic device, and a storage medium. The image detection method comprises: determining whether there is a target object in an image to be subjected to detection; in response to there being a target object in said image, extracting structure key point information of the target object in said image; and on the basis of the structure key point information and said image, determining the classification of the target object in said image.

Description

Image detection method, device, electronic device and storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese invention patent application with application number 202311108453.1, entitled “Image detection method, device, electronic device and storage medium” and filing date August 30, 2023, and the entire application is incorporated herein by reference. Technical Field

[0003] The present disclosure relates to the field of computer technology, and in particular to an image detection method, device, electronic device, and storage medium. Background Art

[0004] Currently, defects in virtual portraits in AI-generated images are a common problem. These defects, such as an abnormal number of fingers, abnormal limb proportions, and unnatural joints, seriously affect the quality of AI-generated images. Currently, there is a lack of an effective method to determine whether virtual portraits in AI-generated images contain defects.

[0005] Summary of the Invention

[0006] This summary is provided to briefly introduce concepts that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] In a first aspect, according to one or more embodiments of the present disclosure, there is provided an image detection method, comprising:

[0008] Determine whether a target object exists in the image to be tested;

[0009] In response to the target object existing in the image to be measured, extracting structural key point information of the target object in the image to be measured;

[0010] Based on the structural key point information and the image to be measured, the classification of the target object in the image to be measured is determined.

[0011] In a second aspect, according to one or more embodiments of the present disclosure, there is provided an image detection device, comprising:

[0012] A determination unit, configured to determine whether a target object exists in the image to be tested;

[0013] an extraction unit, configured to extract structural key point information of the target object in the image to be measured in response to the target object existing in the image to be measured;

[0014] A classification unit is configured to determine the classification of the target object in the image to be tested based on the structural key point information and the image to be tested.

[0015] In a third aspect, according to one or more embodiments of the present disclosure, an electronic device is provided, comprising: at least one memory and at least one processor; wherein the memory is used to store program code, and the processor is used to call the program code stored in the memory to enable the electronic device to execute the method provided according to one or more embodiments of the present disclosure.

[0016] In a fourth aspect, according to one or more embodiments of the present disclosure, a non-transitory computer storage medium is provided, wherein the non-transitory computer storage medium stores program code, and when the program code is executed by a computer device, the computer device executes the method provided according to one or more embodiments of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale.

[0018] FIG1 is a flow chart of an image detection method provided by an embodiment of the present disclosure;

[0019] FIG2 is a flow chart of an image detection method provided by another embodiment of the present disclosure;

[0020] FIG3 is a flow chart of an image detection method provided by another embodiment of the present disclosure;

[0021] FIG4 is a schematic structural diagram of an image detection device provided by an embodiment of the present disclosure;

[0022] FIG5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0023] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0024] It should be understood that the steps described in the embodiments of the present disclosure can be performed in a different order and / or in parallel. In addition, the embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0025] As used herein, the term "including" and its variations are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The term "responsive to" and related terms refer to a signal or event being affected to a certain extent by another signal or event, but not necessarily completely or directly. If event x occurs "responsive to" event y, then x may be directly or indirectly responsive to y. For example, the occurrence of y may ultimately lead to the occurrence of x, but there may be other intermediate events and / or conditions. In other cases, y may not necessarily lead to the occurrence of x, and x may occur even if y has not yet occurred. In addition, the term "responsive to" may also mean "at least partially responsive to".

[0026] The term "determine" broadly encompasses a variety of actions, and may include obtaining, calculating, computing, processing, deriving, investigating, searching (e.g., searching in a table, database, or other data structure), ascertaining, and similar actions, and may also include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory), and similar actions, as well as parsing, selecting, choosing, establishing, and similar actions. Other terms are defined below. Other terms are defined below.

[0027] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) must comply with the provisions of relevant laws and regulations.

[0028] It is understood that before using the technical solutions disclosed in each embodiment of the present disclosure, the user should be informed of the type, scope of use, and usage scenarios of the personal information involved in the present disclosure and obtain the user's authorization in an appropriate manner in accordance with relevant laws and regulations. For example, in response to receiving an active request from the user, a prompt message is sent to the user to clearly inform the user that the operation requested will require the acquisition and use of the user's personal information, so that the user can independently choose whether to provide personal information to the electronic device, application, server, storage medium, or other software or hardware that performs the operation of the technical solution of the present disclosure based on the prompt message.

[0029] As an optional but non-limiting implementation, in response to receiving a user's active request, a prompt message may be sent to the user, for example, in the form of a pop-up window, in which the prompt message may be presented in text form. Furthermore, the pop-up window may also include a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0030] It is understood that images generated by the methods provided in various embodiments of the present disclosure should be processed in accordance with relevant laws and regulations. For example, technical measures may be taken in accordance with regulations to add a logo that does not affect user use, or prominent logos may be placed in reasonable locations and areas in accordance with regulations to inform the public of deep compositing.

[0031] It is understandable that the above notification and user authorization process, as well as image processing, are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0032] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0033] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0034] For the purposes of this disclosure, the phrase "A and / or B" means (A), (B), or (A and B).

[0035] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0036] There is currently a lack of targeted solutions for identifying limb defects in virtual portraits in AI-generated content. Related technologies generally treat it as a classification problem, that is, a classification model is trained using sample images and corresponding manually annotated labels indicating whether the limbs have defects, and the classification model is used to identify the images. However, since identifying limb defects is inherently difficult, the normal state of limbs varies from different perspectives and under different human body movements, which requires the classification model to have a deep understanding of the state of the limbs. The solutions provided by related technologies that train models based only on samples and manually annotated labels can easily lead to the classification model overfitting to other easily fit features in the image (such as the overall complexity of the picture) during the training process, resulting in the trained classification model not truly understanding limb defects. In addition, since mislabeling is inevitable when manually labeling sample data, the presence of this data noise further increases the probability of the model fitting to other features during the training process.

[0037] In response to the above problems, the present disclosure provides an image detection method, which classifies the target object based on the image to be tested and the structural key point information of the target object extracted from the image to be tested, so that the classification model can pay more attention to the target object during the training and discrimination process, avoid overfitting of the model to other features, and thereby enhance the robustness of the model to data annotation noise and improve the accuracy of target object classification.

[0038] Referring to FIG. 1 , FIG. 1 shows a flowchart of an image detection method 100 provided in an embodiment of the present disclosure. The method 100 includes steps S110 to S130 .

[0039] Step S110: determining whether a target object exists in the image to be tested;

[0040] Step S120: In response to the target object existing in the image to be tested, extracting structural key point information of the target object in the image to be tested;

[0041] Step S130: Determine the classification of the target object in the image to be tested based on the structural key point information and the image to be tested.

[0042] The structural key points of the target object are used to determine the structural morphology of the target object. In one specific embodiment, the target object may include a virtual human portrait, which may include, for example, a human portrait included in AI-generated content. Accordingly, the structural key point information of the target object may include virtual human key point information, such as information on some or all key points of the nose, eyes, ears, shoulders, elbows, wrists, hips, knees, and ankles.

[0043] In some embodiments, step S130 may be used to determine whether there are defects in the virtual human portrait in the image to be tested. The following is an exemplary description of a classification scenario for determining whether there are defects in the virtual human portrait.

[0044] 2 , which shows a method 200 for determining whether a virtual human portrait in an image has defects, including steps S201 to S204 .

[0045] In step 201, an image to be tested is acquired. For example, the image to be tested may include artificial intelligence generated content or other virtual images.

[0046] In step 202, human body detection is performed on the image to be tested to determine whether a virtual human portrait exists. In some embodiments, the presence of a virtual human portrait in the image to be tested can be determined using a target detection algorithm. The task of target detection is to find all targets (objects) of interest in the image and determine their categories and locations. The target detection algorithm can adopt, for example, a region-based convolutional neural network target detection algorithm, a single-step multi-frame target detection algorithm, a retinal network-based target detection algorithm, etc., but the present disclosure is not limited thereto.

[0047] If it is determined that no virtual human portrait exists, it can be directly determined that no virtual human portrait defect exists in the image to be tested. If it is determined that a virtual human portrait exists, step 203 is executed.

[0048] In step 203, virtual human key point information is extracted from the image to be tested. In some embodiments, the virtual human key point information can be extracted using a human key point estimation algorithm. The human key point estimation algorithm can employ a human pose estimation method based on a high-resolution network, a convolutional pose machine, a stacked hourglass network, or a cascaded pyramid network, but the present disclosure is not limited thereto. In one specific embodiment, eight coordinates corresponding to the hands, elbows, feet, and legs of the virtual human figure in the image to be tested can be extracted as the virtual human key point information.

[0049] In step 204, the virtual human key point information and the image to be tested can be used as input to a trained classification model to determine whether the virtual human portrait in the image to be tested has defects. In a specific embodiment, the classification model is trained to be able to determine the confidence level of the presence of defects in the virtual human portrait in the image based on the input image and the virtual human key point information. For example, the sample image and the virtual human key point information extracted based on the sample image can be used as input to the model, and the manually annotated label corresponding to the sample image (for example, whether the sample image has defects in the virtual human portrait) is used as the expected output to train the classification model.

[0050] The manually annotated labels can be related to whether the sample image has virtual portrait defects, and can also refer to other classification types according to other application needs, which are not limited in this disclosure. In a specific implementation, the positive label can be 1 and the negative label can be 0.

[0051] It should be noted that the image detection method provided by the present disclosure can be used not only to determine whether a virtual human portrait has defects in classification scenarios, but also to determine the classification of target objects with specific structures such as animals, robots, and virtual images, and the present disclosure does not impose any restrictions on this.

[0052] According to one or more embodiments of the present disclosure, by classifying the target object based on the image to be tested and the structural key point information of the target object extracted from the image to be tested, the classification model can pay more attention to the target object during the training and discrimination process, and can avoid overfitting of the model to other features, thereby enhancing the robustness of the model to data annotation noise and improving the accuracy of target object classification.

[0053] In some embodiments, step S130 includes:

[0054] Step A1: generating a first target feature based on the image to be measured, where the first target feature includes a feature associated with the target object;

[0055] Step A2: performing feature conversion on the structural key point information to obtain a second target feature;

[0056] Step A3: generating a third target feature based on the first target feature and the second target feature;

[0057] Step A4: Determine the classification of the target object in the image to be measured based on the third target feature.

[0058] In some embodiments, the number of channels corresponding to the second target feature is the same as the number of channels corresponding to the first target feature. By making the number of channels corresponding to the second target feature the same as the number of channels corresponding to the first target feature, it is convenient to subsequently connect the first target feature and the second target feature to obtain the third target feature.

[0059] In some embodiments, before determining the classification of the target object in the image to be tested based on the third target feature, the third target feature is subjected to semantic unit fusion processing so that the semantic units in the third target feature can fuse the information of other semantic units in the third target feature, that is, the semantic units can be fused with each other. A semantic unit is a unit with semantic information, such as a token. Further, the classification of the target object in the image to be tested can be determined based on the third target feature after the semantic unit fusion processing.

[0060] In this embodiment, by performing semantic unit fusion processing on the third target feature, the features from the image to be tested and the features from the structural key point information can be further fused to a certain extent at the semantic unit level, thereby further enhancing the classification model's attention to the area of ​​the target object part in the image.

[0061] In some specific embodiments, the self-attention mechanism in the conversion network can be used to determine the fusion weight based on the correlation between each semantic unit to achieve semantic unit fusion, or the semantic unit fusion can be achieved by expanding the size of the convolution kernel in the convolutional neural network, or by full connection between tokens in the multi-layer perceptron to achieve semantic unit fusion, but the present disclosure is not limited to this.

[0062] It should be noted that the semantic unit fusion processing in this embodiment can be a global semantic unit fusion or a fusion of some semantic units, and this disclosure does not limit this.

[0063] In some embodiments, step A4 includes:

[0064] Step a41: inputting the third target feature into a classification network to obtain a confidence level of the presence of a defect in the target object and / or a confidence level of the absence of a defect in the target object;

[0065] Step a42: based on the confidence level of the target object having defects and / or the confidence level of the target object not having defects, obtain the category of the target object.

[0066] In some embodiments, before the third target feature is input into the classification network, other processing may be performed on the third target feature, such as semantic unit fusion processing, preset dimension averaging processing, etc., which is not limited in this disclosure.

[0067] 3 , an exemplary description is given below using a classification scenario of determining whether a virtual human portrait has defects.

[0068] In the first step, a normalized tensor Ti is obtained based on the virtual human keypoint information in the image to be tested. This tensor can be expressed as (B, N, C), where B represents the batch size of the training samples, that is, the number of data (samples) passed to the program for training at a single time; N is the number of semantic units; and C is the number of channels. For example, assuming that the coordinates (x, y) of eight human keypoints in the x and y directions are obtained for each image to be tested, N can be 8 and C can be 2.

[0069] In the second step, the tensor Ti is input into the feature extraction network for feature conversion to obtain the tensor Tf, which can be expressed as (B, N1, C1). In one embodiment, the feature extraction network can be a conversion network, which is a neural network model based on the self-attention mechanism for processing sequence data.

[0070] In the third step, the image to be tested (such as an RGB image) is input into the backbone network to extract features, and a tensor Tm is obtained, which can be expressed as (B, N2, C1). A well-trained backbone network is expected to be used to extract features associated with the classification task from the image to be tested. In the classification scenario of virtual portraits, it is used to extract features about the structure and quantity of virtual portraits. Of course, in practical applications, the features extracted by the backbone network may also include features that are not expected to be extracted, such as features of buildings or other objects in the image that are not related to the virtual portrait. In a specific embodiment, the backbone network can adopt a visual conversion network or other image feature extraction network, but the present disclosure is not limited to this.

[0071] The fourth step is to concatenate tensors Tf and Tm along the dimension of the semantic unit to obtain tensor Tc, which can be expressed as (B, N1 + N2, C1). For example, the concatenation operation can be implemented by a concatenation function (Concat), which can concatenate features along a specified dimension.

[0072] In the fifth step, the tensor Tc is input into the feature fusion network to obtain the tensor To, which can be expressed as (B, N3, C2). The feature fusion network can be used to perform semantic unit fusion processing on the input features, so that each semantic unit in the feature can be fused based on its correlation with other semantic units, thereby improving the degree of information interaction. Exemplarily, the feature fusion network can be a transformation network.

[0073] In the sixth step, the tensor To is averaged over the semantic unit dimension (i.e., the "N" in (B, N, C)) and fed into the classification head network to obtain a final classification of whether the virtual portrait has defects. The classification result can be expressed as the confidence level of "the virtual portrait has defects" and / or the confidence level of "the virtual portrait does not have defects." Exemplarily, the classification head network can be a fully connected layer. In this step, by averaging the tensor To over the semantic unit dimension, it can be easily processed by the classification head network.

[0074] In this embodiment, the classification network includes a feature extraction network, a backbone network, a feature fusion network, and a classification head network. Exemplarily, when training the classification network, the sample image and its corresponding virtual human key point information can be input into the backbone network and the feature extraction network respectively, and the prediction results are obtained by passing through the feature fusion network and the classification head network in sequence. Based on the loss (e.g., cross entropy loss) between the prediction result and the label corresponding to the sample image, the parameters of the feature extraction network, the backbone network, the feature fusion network, and the classification head network are adjusted to converge the loss function. In a specific embodiment, the backbone network can be initialized with pre-trained parameters to accelerate convergence.

[0075] Accordingly, referring to FIG4 , an image detection device 400 is provided according to an embodiment of the present disclosure, including:

[0076] A determination unit 401 is used to determine whether a target object exists in the image to be tested;

[0077] An extraction unit 402 is configured to extract structural key point information of the target object in the image to be measured in response to the target object existing in the image to be measured;

[0078] The classification unit 403 is configured to determine the classification of the target object in the image to be tested based on the structural key point information and the image to be tested.

[0079] In some embodiments, the target object includes a virtual human portrait, and the structural key point information of the target object includes virtual human body key point information.

[0080] In some embodiments, the classification unit comprises:

[0081] a feature extraction subunit, configured to generate a first target feature based on the image to be measured, wherein the first target feature includes a feature associated with the target object;

[0082] A feature conversion subunit, configured to perform feature conversion on the structural key point information to obtain a second target feature;

[0083] a feature generating subunit, configured to generate a third target feature based on the first target feature and the second target feature; and

[0084] A classification subunit is configured to determine the classification of the target object in the image to be measured based on the third target feature.

[0085] In some embodiments, the classification subunit is used to input the third target feature into a classification network to obtain a confidence level that a defect exists in the target object and / or a confidence level that a defect does not exist in the target object, and based on the confidence level that a defect exists in the target object and / or the confidence level that a defect does not exist in the target object, obtain the category of the target object.

[0086] In some embodiments, the image detection device further comprises:

[0087] a feature fusion subunit, configured to perform semantic unit fusion processing on the third target feature before determining the classification of the target object in the image to be measured based on the third target feature, so that the semantic unit in the third target feature can be integrated with information of other semantic units in the third target feature;

[0088] The classification subunit is used to determine the classification of the target object in the image to be tested based on the third target feature after the semantic unit fusion processing.

[0089] In some embodiments, the determining unit is further configured to determine that the image to be tested does not have a target object defect in response to the target object not being present in the image to be tested.

[0090] For the embodiments of the device, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, and the modules described as separation modules may or may not be separate. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art can understand and implement it without paying any creative work.

[0091] Accordingly, according to one or more embodiments of the present disclosure, there is provided an electronic device, including:

[0092] at least one memory and at least one processor;

[0093] The memory is used to store program codes, and the processor is used to call the program codes stored in the memory to enable the electronic device to execute the image detection method provided according to one or more embodiments of the present disclosure.

[0094] Accordingly, according to one or more embodiments of the present disclosure, a non-transitory computer storage medium is provided, which stores program code, and the program code can be executed by a computer device to enable the computer device to perform the image detection method provided according to one or more embodiments of the present disclosure.

[0095] Reference is now made to FIG5 , which illustrates a schematic diagram of the structure of an electronic device (e.g., a terminal device or server) 800 suitable for implementing embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The electronic device illustrated in FIG5 is merely an example and should not limit the functionality or scope of use of the embodiments of the present disclosure.

[0096] As shown in Figure 5, the electronic device 800 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage device 808 into a random access memory (RAM) 803. Various programs and data required for the operation of the electronic device 800 are also stored in the RAM 803. The processing device 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0097] Typically, the following devices may be connected to the I / O interface 805: an input device 806 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 807 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 808 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 809. The communication device 809 may allow the electronic device 800 to communicate with other devices wirelessly or by wire to exchange data. Although FIG. 5 shows the electronic device 800 with various devices, it should be understood that not all of the devices shown are required to be implemented or present. More or fewer devices may alternatively be implemented or present.

[0098] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication device 809, or installed from the storage device 808, or installed from the ROM 802. When the computer program is executed by the processing device 801, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0099] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0100] In some embodiments, the client and server can communicate using any currently known or later developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or later developed network.

[0101] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0102] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method of the present disclosure.

[0103] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0104] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0105] The units involved in the embodiments described in this disclosure may be implemented in software or hardware, wherein the name of a unit does not necessarily limit the unit itself.

[0106] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0107] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0108] According to one or more embodiments of the present disclosure, an image detection method is provided, comprising: determining whether a target object exists in an image to be tested; extracting structural key point information of the target object in the image to be tested in response to the presence of the target object in the image to be tested; and determining a classification of the target object in the image to be tested based on the structural key point information and the image to be tested.

[0109] According to one or more embodiments of the present disclosure, the target object includes a virtual human portrait, and the structural key point information of the target object includes virtual human body key point information.

[0110] According to one or more embodiments of the present disclosure, based on the structural key point information and the image to be measured, determining the classification of the target object in the image to be measured includes: generating a first target feature based on the image to be measured, the first target feature including a feature associated with the target object; performing feature conversion on the structural key point information to obtain a second target feature; generating a third target feature based on the first target feature and the second target feature; and determining the classification of the target object in the image to be measured based on the third target feature.

[0111] According to one or more embodiments of the present disclosure, determining the classification of the target object in the image to be tested based on the third target feature includes: inputting the third target feature into a classification network to obtain a confidence level that a defect exists on the target object and / or a confidence level that a defect does not exist on the target object; and obtaining a category of the target object based on the confidence level that a defect exists on the target object and / or the confidence level that a defect does not exist on the target object.

[0112] According to one or more embodiments of the present disclosure, the method further includes: before determining the classification of the target object in the image to be tested based on the third target feature, performing semantic unit fusion processing on the third target feature so that the semantic unit in the third target feature can fuse information of other semantic units in the third target feature; determining the classification of the target object in the image to be tested based on the third target feature includes: determining the classification of the target object in the image to be tested based on the third target feature after the semantic unit fusion processing.

[0113] The method provided according to one or more embodiments of the present disclosure further includes: in response to the target object not existing in the image to be tested, determining that the image to be tested does not have a target object defect.

[0114] According to one or more embodiments of the present disclosure, there is provided an image detection device, comprising: a determination unit for determining whether a target object exists in an image to be tested; an extraction unit for extracting structural key point information of the target object in the image to be tested in response to the presence of the target object in the image to be tested; and a classification unit for determining a classification of the target object in the image to be tested based on the structural key point information and the image to be tested.

[0115] According to one or more embodiments of the present disclosure, an electronic device is provided, comprising: at least one memory and at least one processor; wherein the memory is used to store program code, and the processor is used to call the program code stored in the memory so that the electronic device executes the image detection method provided according to one or more embodiments of the present disclosure.

[0116] According to one or more embodiments of the present disclosure, a non-transitory computer storage medium is provided, which stores program code. When the program code is executed by a computer device, the computer device executes the image detection method provided according to one or more embodiments of the present disclosure.

[0117] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

[0118] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details have been included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0119] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Claims

1. An image detection method, comprising: Determine whether there is a target object in the image to be tested; In response to the target object existing in the image to be tested, extracting structural key point information of the target object in the image to be tested; Based on the structural key point information and the image to be tested, the classification of the target object in the image to be tested is determined. 2 . The method according to claim 1 , wherein the target object comprises a virtual human portrait, and the structural key point information of the target object comprises virtual human key point information.

3. The method according to claim 1, wherein determining the classification of the target object in the image to be tested based on the structural key point information and the image to be tested comprises: generating a first target feature based on the image to be measured, wherein the first target feature includes a feature associated with the target object; Performing feature conversion on the structural key point information to obtain a second target feature; generating a third target feature based on the first target feature and the second target feature; The classification of the target object in the image to be tested is determined based on the third target feature.

4. The method according to claim 3, wherein determining the classification of the target object in the image to be tested based on the third target feature comprises: Inputting the third target feature into a classification network to obtain a confidence level that a defect exists in the target object and / or a confidence level that a defect does not exist in the target object; Based on the confidence level that a defect exists on the target object and / or the confidence level that a defect does not exist on the target object, a category of the target object is obtained.

5. The method according to claim 3, wherein The method further comprises: Before determining the classification of the target object in the image to be tested based on the third target feature, performing semantic unit fusion processing on the third target feature so that the semantic unit in the third target feature can fuse information of other semantic units in the third target feature; The determining the classification of the target object in the image to be tested based on the third target feature includes: determining the classification of the target object in the image to be tested based on the third target feature after semantic unit fusion processing.

6. The method according to claim 1, further comprising: In response to the target object not existing in the image to be tested, determining that the image to be tested does not have a target object defect defect.

7. An image detection device, comprising: A determination unit, used to determine whether there is a target object in the image to be tested; an extraction unit, configured to extract structural key point information of the target object in the image to be tested in response to the target object existing in the image to be tested; A classification unit is used to determine the classification of the target object in the image to be tested based on the structural key point information and the image to be tested.

8. An electronic device comprising: at least one memory and at least one processor; The memory is used to store program codes, and the processor is used to call the program codes stored in the memory to enable the electronic device to execute the method according to any one of claims 1 to 6.

9. A non-transitory computer storage medium, wherein The non-transitory computer storage medium stores a program code, and when the program code is executed by a computer device, the computer device executes the method according to any one of claims 1 to 6.

10. A computer program product, comprising a program code, wherein when the program code is executed by a computer device, the computer device executes the method according to any one of claims 1 to 6.

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