Image processing system and method for a display device

Abstract

This application provides an image processing system and method for a display device. The method includes: acquiring image information to be processed; performing image analysis processing on the image information to be processed to obtain target image detection result information.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically to an image processing system and method for display devices. Background Technology

[0002] In image display, distortion is a crucial indicator. Current techniques involve manually observing and recording the spectrometer scale readings for the corresponding field of view, then manually calculating the distortion data using a formula. This method is cumbersome and can only test the distortion of one field of view at a time, resulting in low efficiency. Summary of the Invention

[0003] This application provides an image detection method that can effectively detect image distortion.

[0004] In a first aspect, this application provides an image detection method, the method comprising:

[0005] Obtain the image information to be processed;

[0006] The image information to be processed is subjected to image analysis processing to obtain target image detection result information.

[0007] Secondly, this application also provides an image detection system, the system comprising:

[0008] The acquisition module is used to acquire information about the image to be processed.

[0009] The detection module is used to perform image analysis and processing on the image information to be processed, and obtain the target image detection result information.

[0010] Thirdly, this application also provides a terminal device, the terminal device including a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the steps in any of the image detection methods described above.

[0011] Fourthly, this application also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement the steps of any of the image detection methods described above. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a scene diagram of the image detection system provided in the embodiments of this application;

[0014] Figure 2 This is a schematic flowchart of one embodiment of the image detection method in this application;

[0015] Figure 3 This is a schematic diagram of a functional module of the image detection system in an embodiment of this application;

[0016] Figure 4 This is a schematic diagram of the structure of the terminal device in the embodiments of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0019] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. Furthermore, it is understood that in the specific embodiments of this application, user information, user data, and other related data are involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0020] To enable any person skilled in the art to implement and use this application, the following description is provided. In this description, details are set forth for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be implemented without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0021] This application provides an image detection method, system, device, and storage medium, which are described in detail below.

[0022] Please see Figure 1 , Figure 1 This is a schematic diagram of a scene for an image detection system provided in an embodiment of this application. The image detection system may include a terminal device 100 and a capturing device 200, and the capturing device 200 may transmit data to the terminal device 100. Figure 1 The terminal device 100 can acquire the image information to be processed captured by the imaging device 200 in order to execute the image detection method in this application.

[0023] In this embodiment, the terminal device 100 may include, but is not limited to, a desktop computer, a portable computer, a web server, a PDA (Personal Digital Assistant), a tablet computer, a wireless terminal device, an embedded device, etc. The imaging device 200 includes any type of image sensor.

[0024] In the embodiments of this application, the terminal device 100 and the shooting device 200 can communicate through any communication method, including but not limited to mobile communication based on the 3rd Generation Partnership Project (3GPP), Long Term Evolution (LTE), and Worldwide Interoperability for Microwave Access (WiMAX), or computer network communication based on the TCP / IP Protocol Suite (TCP / IP) and User Datagram Protocol (UDP).

[0025] It should be noted that, Figure 1 The schematic diagram of the image detection system shown is merely an example. The image detection system and scenario described in this application are for the purpose of more clearly illustrating the technical solutions of this application and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the evolution of image detection systems and the emergence of new business scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0026] like Figure 2 As shown, Figure 2This is a schematic flowchart of an embodiment of the image detection method in this application. The image detection method may include the following steps 201-202:

[0027] 201. Obtain the image information to be processed.

[0028] In this application embodiment, the image information to be processed can be any type of image information, such as jpg, gif, png, or any other format. Specific embodiments of this application are not limited in this regard. The image information to be processed can be obtained from the target storage medium or through other transmission methods. Specific embodiments of this application are also not limited in this regard.

[0029] It should be noted that, in the embodiments of this application, the image information to be processed may include objects of any shape. After the corresponding display device acquires and displays the image information to be processed, it can determine whether the objects in the image information to be processed can be displayed normally during the display process, thereby determining whether there is a distortion problem in the image information to be processed.

[0030] It should be noted that, in the embodiments of this application, when the display device displays an image, the image may be displayed incorrectly due to hardware problems with the display itself, display driver problems, or problems with the transmission signal line. For example, a circular object should be displayed, but the actual display effect is an ellipse. Therefore, the problem of image distortion is the problem of image display error.

[0031] 202. Perform image analysis and processing on the image information to be processed to obtain the target image detection result information.

[0032] In this embodiment, an image sensor can be used to capture the image information to be processed displayed on the display device, thereby simulating a user viewing the monitor through the image sensor. Subsequently, detection processing, such as distortion detection, can be performed based on the image information captured by the image sensor. In this embodiment, distortion detection includes the detection of object deformation.

[0033] In this process, the objects in the image information to be processed can be circular. Furthermore, during distortion detection, an image recognition method can be used. This image recognition model can determine whether a circle is truly circular, and not an ellipse or other irregular circle. Based on this, after the image sensor acquires the image information to be processed, any image recognition method can be used to detect whether the circles in the image information are distorted, thereby determining whether the display exhibits distortion.

[0034] It should be noted that when determining that the circle in the image to be processed is not an irregular circle by the image recognition method, the difference between the curvature of the irregular circle and the curvature of the normal circle can be calculated, thereby determining the degree of distortion. The degree of distortion can be understood as the target image detection result information in the embodiments of this application.

[0035] Furthermore, it should be noted that the object graphic in this application embodiment may not be circular; it can be any other type of graphic. When the object graphic is any other graphic, the image recognition algorithm can identify whether the lines in the graphic are distorted, thereby performing distortion detection. Specific embodiments of this application are not limited in this regard.

[0036] The image detection method provided in this application can acquire an image to be processed and perform detection processing on the image to be processed, thereby determining the distortion target image detection result information of the image to be processed, and realizing automatic detection of image distortion.

[0037] To better implement the embodiments of this application, in one embodiment of this application, obtaining the image information to be processed includes:

[0038] Acquire first image information; perform correction processing on the first image information to obtain image information to be processed.

[0039] As can be seen from the above embodiments, the image information to be processed can be obtained by capturing images using an image sensor. However, image sensors and other devices may themselves have some distortion problems when capturing images. Therefore, in order to improve the accuracy of detecting distortion in the image information to be processed, it is necessary to eliminate the distorted pixels generated by the image sensor. In the embodiments of this application, the first image information is an image captured by the image sensor that has not been corrected. Therefore, it is necessary to correct the first image information before obtaining the image information to be processed.

[0040] In this embodiment, the calibration parameter information can first be obtained from the image sensor model, such as the image sensor's instruction manual or the parameter information disclosed by the manufacturer. Then, the parameter information is passed to the OpenCV function `GetOptimalNewCameraMatrix` to obtain a mapping matrix, which is a target correction factor. Afterwards, the mapping matrix and the first image information can be passed to the OpenCV function `Undistort` to obtain the first image information of the target after camera distortion correction.

[0041] Furthermore, to improve the accuracy of image recognition, the embodiments of this application can also perform grayscale and binarization processing on the target first image information to obtain a black and white image information to be processed. For example, the background of the obtained image information to be processed can be black, while the object graphic can be white. In subsequent image recognition, this can effectively help the recognition algorithm determine the shape of the object graphic, thereby facilitating the determination of whether the graphic has been distorted.

[0042] In this embodiment, when distortion detection is to be performed, the display device can load the image information containing the object graphic through a third-party storage device or the built-in storage device when it receives a distortion detection instruction. Alternatively, relevant personnel can retrieve the image information to be detected to obtain the image information. Specific embodiments of this application are not limited in this regard.

[0043] To better implement the embodiments of this application, in one embodiment, the image information to be processed includes several object representations. Image analysis processing is performed on the image information to be processed to obtain target image detection result information, including:

[0044] Contour detection is performed on each object representation in the image information to be processed to obtain the contour representation of each object representation; position recognition processing is performed on each object representation based on the contour representation to obtain the position information of each object representation; and the target image detection result information is determined based on the position information.

[0045] The above embodiments provide a method for distortion detection by recognizing whether an image is regular. However, some image recognition algorithms consume significant computing resources, placing a heavy burden on devices with limited computing power. Therefore, to alleviate the computational burden, this application also provides a distortion detection method.

[0046] Specifically, in practice, distortion includes both shape distortion and positional distortion. That is, when the position of a displayed object does not match a predetermined position, it also falls under the category of distortion. Based on this, the image information to be processed can include multiple object representations, which can be small-diameter dots rather than circles as in the above embodiments. These dots can also fill the entire image information to be processed. Therefore, to determine the position information of the dots, the outline representation of each dot can be determined first, and then the position information of each object representation in the image can be determined based on the outline representation. For example, in this embodiment, the OpenCV FindContours function can be called to determine the outline of each dot. Then, the center coordinates of each dot can be found using the relevant formula for calculating the center, thus obtaining the actual position information of each dot.

[0047] Furthermore, when filling the image information to be processed with dots, the theoretical position of each dot can be determined based on the resolution of the image information. However, when displayed on a monitor and captured by an image sensor, the theoretical position of each dot may not match its actual position due to potential display distortion. The above description, however, allows for the determination of the actual position information of each dot. Therefore, it is possible to determine whether the theoretical position information of each dot, determined during the setup, matches its corresponding actual position information, thereby determining the target image detection result information regarding whether the corresponding dot exhibits distortion.

[0048] To better implement the embodiments of this application, in one embodiment of this application, each object representation includes a first object representation and several second object representations, wherein the first object representation represents the image information of the image center region in the image information to be processed;

[0049] Based on location information, the target image detection result information is determined, including:

[0050] Based on the location information of the first object representation and the location information of several second object representations, the second target distance information between each second object representation and the first object representation is determined; based on the second target distance information, the target image detection result information is determined.

[0051] In the above embodiments, a scheme is provided to determine a theoretical position information for each dot when setting the image information to be processed, and then determine the target image detection result information whether there is distortion based on the theoretical position information and the corresponding actual position information.

[0052] This application embodiment also provides another implementation method to determine whether each object representation in the image information to be processed is distorted. For example, if each object representation is a dot, then it is determined whether each dot is distorted. For example, a dot is set at the center point of the image information to be processed as a reference dot, i.e., the first object representation. Then, the other dots, i.e., the second object representations, are evenly distributed in the image information to be processed. For example, the dots are laid flat on the image to be processed with an average distance of 2 millimeters between each pair of dots. Then, theoretically, the theoretical distance between a dot and the dot at the center point of the image information to be processed can be directly calculated. For example, the number of dots between the current second object representation and the first image representation at the center point is calculated, and the number of dots is multiplied by 2 millimeters to obtain the theoretical distance. The advantage of this is that it eliminates the need to set a theoretical position for each dot, simplifying the detection process.

[0053] To better implement the embodiments of this application, in one embodiment of this application, determining the second target distance information between each second object representation and the first object representation includes:

[0054] Determine the first target distance information between each second object representation and the first object representation; determine several second object representations into several object representation sets based on the first target distance information; average the first target distance information of the second object representations in each object representation set to obtain the second target distance information of the second object representations in each object representation set.

[0055] In practice, the camera's shooting center may not be precisely aligned with the center of the screen. Therefore, to reduce errors, data averaging is necessary. For example, a coordinate axis can be set in the image information to be processed, and then, based on a set interval, various dots can be placed in the image information. Then, the dots can be grouped according to their coordinate information. For example, dots with the same absolute coordinate value but different signs can be grouped into one object representation set. Next, the average actual distance from each dot to the coordinate dot in each object representation set can be calculated. This average actual distance can then be used as the second target distance information for each object representation in that object representation set. Finally, the target image detection result information can be calculated according to the above embodiment.

[0056] To better implement the embodiments of this application, in one embodiment of this application, each second object is represented as being distributed in the image information to be processed according to unit distance information;

[0057] Based on the second target distance information, the target image detection result information is determined, including:

[0058] Determine the coordinate information and unit distance information corresponding to each second object representation, and determine the third target distance information between each second object representation and the first object representation; based on the second target distance information and the third target distance information, determine the target image detection result information.

[0059] The above embodiments provide a scheme for evenly distributing dots across the image information to be processed at preset intervals. To further simplify the computational data, in this embodiment, dots may be placed only on the target coordinate axis, while dots are not placed in areas outside the coordinate axis.

[0060] Therefore, after each second object representation is set only on the coordinate axes, it can be set according to the field of view distance, rather than according to the ordinary spacing interval. In this case, excluding the reference point, when other points are set on the coordinate axes, based on the reference point as the center of symmetry, symmetrical points can be found on the x-axis, and similarly on the y-axis. These symmetrical points are then treated as a set of object representations, the average field of view spacing is calculated, and then the distortion information is calculated according to any of the above embodiments.

[0061] It should be noted that, in any embodiment of this application, the scheme for calculating the distortion value based on the distance information of the third target and the distance information of the second target can refer to formula (1), which is shown below:

[0062] (Dn / (D1*n)-1)*100%=S……(1)

[0063] Where n is the ordinal number of other dots, for example, with the ordinal number of the reference dot as 0, the ordinal numbers of the dots extending outwards from the reference dot in the four directions of the coordinate axis increase by 1 sequentially; Dn is the actual distance information between each dot and the reference dot, i.e., the second target distance information; D1 represents the unit distance information or the set distance interval; D1*n represents the third target distance information; S is the calculated distortion value. Wherein, assuming that there is no distortion in the image, the image will be displayed normally, and the theoretical distance between the dots should be the unit distance information multiplied by the number of dots between the two dots. Therefore, the meaning of formula (1) is that if the actual distance Dn between two dots is more equal to the theoretical distance between the two dots, the degree of distortion is smaller.

[0064] To better implement the embodiments of this application, in one embodiment, image analysis processing is performed on the image information to be processed to obtain target image detection result information, including:

[0065] Responding to the user's target detection command, which indicates that the target region in the image information to be processed should be detected; according to the target detection command, the target region in the image information to be processed is detected and processed to obtain the target image detection result information.

[0066] Furthermore, the distortion value of each dot can be calculated according to the above formula (1), at which point the coordinate information of each dot and its corresponding distortion value can be obtained. Then, the coordinate information of each dot and its corresponding distortion value can be fitted to obtain a mapping function consisting of position information and distortion value. Subsequently, in subsequent processes, if relevant personnel need to query the distortion situation of a specific location, they can input the coordinate information of that location into the mapping function to obtain the corresponding distortion value.

[0067] For example, the theoretical field of view distance of coordinates (x1, y1) can be set to a in advance. If the actual field of view distance of (x1, y1) is measured to be b, and the distortion value is 9.3154% according to the above formula (1), if the upper limit of the specification value at the position (x1, y1) is set to 9%, then the distortion test corresponding to the coordinate value (x1, y1) will fail and can be displayed as Fail. If the upper limit of the specification value at the position (x1, y1) is set to 10%, then the distortion test will pass and can be displayed as Pass.

[0068] To better implement the image detection method in the embodiments of this application, an image detection system is also provided in the embodiments of this application, such as... Figure 3 As shown, system 300 includes:

[0069] The acquisition module 301 is used to acquire image information to be processed;

[0070] The detection module 302 is used to perform image analysis and processing on the image information to be processed, and obtain the target image detection result information.

[0071] The image detection system provided in this application can acquire the image to be processed through the acquisition module 301 and perform detection processing on the image to be processed through the detection module 302, thereby determining the distortion target image detection result information of the image to be processed, and realizing automatic detection of image distortion.

[0072] In some embodiments of this application, the acquisition module 301 is specifically used for:

[0073] Obtain the first image information;

[0074] The first image information is corrected to obtain the image information to be processed.

[0075] In some embodiments of this application, the image information to be processed includes several object representations; the detection module 302 is specifically used for:

[0076] Contour detection is performed on each object representation in the image information to be processed to obtain the contour representation of each object representation;

[0077] Based on the contour representation, position recognition processing is performed on each object representation to obtain the position information of each object representation;

[0078] Based on location information, the target image detection result information is determined.

[0079] In some embodiments of this application, each object representation includes a first object representation and several second object representations, wherein the first object representation is located at the image center point of the image information to be processed; the detection module 302 is further configured to:

[0080] Based on the location information of the first object representation and the location information of several second object representations, determine the second target distance information between each second object representation and the first object representation;

[0081] Based on the distance information of the second target, the target image detection result information is determined.

[0082] In some embodiments of this application, the detection module 302 is further configured to:

[0083] Determine the first target distance information between each second object representation and the first object representation;

[0084] Based on the distance information of the first target, several second object representations are determined as a set of several object representations;

[0085] The distance information of the first target in the second object representation of each object representation set is averaged to obtain the distance information of the second target in the second object representation of each object representation set.

[0086] In some embodiments of this application, the image information to be processed includes a target coordinate system, the origin of which coincides with the center point of the image, and each second object is represented as being distributed on the coordinate axes of the target coordinate system according to unit distance information; the detection module 302 is further specifically used for:

[0087] Determine the coordinate information and unit distance information corresponding to each second object representation, and determine the third target distance information between each second object representation and the first object representation;

[0088] Based on the distance information of the second target and the distance information of the third target, the target image detection result information is determined.

[0089] In some embodiments of this application, the detection module 302 is further configured to:

[0090] Responding to the user's target detection command, the target detection command indicates that target regions in the image information to be processed should be detected and processed;

[0091] According to the target detection instruction, the target region in the image information to be processed is detected and processed to obtain the target image detection result information.

[0092] This application also provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the steps of any of the image detection methods in this application. This terminal device integrates any of the image detection methods provided in this application, such as... Figure 4 As shown, it illustrates a structural schematic diagram of the terminal device involved in the embodiments of this application. Specifically:

[0093] The terminal device may include components such as a processor 401 with one or more processing cores, a memory 402 with one or more computer-readable storage media, a power supply 403, and an input unit 404. Those skilled in the art will understand that... Figure 4 The terminal device structure shown does not constitute a limitation on the terminal device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0094] The processor 401 is the control center of the terminal device. It connects various parts of the terminal device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 402, and by calling data stored in the memory 402, thereby providing overall monitoring of the terminal device. Optionally, the processor 401 may include one or more processing cores; the processor 401 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. Preferably, the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and application programs, and the modem processor mainly handles wireless communication. It is understood that the aforementioned modem processor may not be integrated into the processor 401.

[0095] The memory 402 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.

[0096] The terminal device also includes a power supply 403 that supplies power to the various components. Preferably, the power supply 403 can be logically connected to the processor 401 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 403 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0097] The terminal device may also include an input unit 404, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0098] Although not shown, the terminal device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 401 in the terminal device loads the executable files corresponding to the processes of one or more applications into the memory 402 according to the following instructions, and the processor 401 runs the applications stored in the memory 402 to realize various functions, such as:

[0099] Obtain the image information to be processed;

[0100] Image analysis and processing are performed on the image information to be processed to obtain the target image detection results.

[0101] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0102] Therefore, embodiments of this application provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in any of the image detection methods provided in embodiments of this application. For example, the computer program loaded by the processor can execute the following steps:

[0103] Obtain the image information to be processed;

[0104] Image analysis and processing are performed on the image information to be processed to obtain the target image detection results.

[0105] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the detailed descriptions of other embodiments above, which will not be repeated here.

[0106] In practice, each of the above units or structures can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities. For the specific implementation of each of the above units or structures, please refer to the previous method embodiments, which will not be repeated here.

[0107] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0108] The above provides a detailed description of an image detection method and system provided by the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and its core ideas. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method, characterized in that, The method includes: Obtain the image information to be processed; The image information to be processed is subjected to image analysis processing to obtain target image detection result information.

2. The method according to claim 1, characterized in that, The acquisition of the image information to be processed includes: Obtain the first image information; The first image information is corrected to obtain the image information to be processed.

3. The method according to claim 1, characterized in that, The image information to be processed includes several object representations; The step of performing image analysis processing on the image information to be processed to obtain target image detection result information includes: Perform contour recognition processing on each of the object representations in the image information to be processed to obtain the contour representation corresponding to each object representation; Based on the contour representation, position recognition processing is performed on each of the object representations to obtain the position information of each of the object representations; Based on the location information, the target image detection result information is determined.

4. The method according to claim 3, characterized in that, Each of the object representations includes a first object representation and several second object representations, wherein the first object representation represents the image information of the central region of the image information to be processed; The step of determining the target image detection result information based on the location information includes: Based on the location information of the first object representation and the location information of several second object representations, determine the second target distance information between each second object representation and the first object representation; Based on the second target distance information, the target image detection result information is determined.

5. The method according to claim 4, characterized in that, Determining the second target distance information between each second object representation and the first object representation includes: Determine the first target distance information between each of the second object representations and the first object representation; Based on the first target distance information, several representations of the second object are determined into a set of several object representations; The first target distance information of the second object representation in each of the object representation sets is averaged to obtain the second target distance information of the second object representation in each of the object representation sets.

6. The method according to claim 4, characterized in that, Each of the second object representations is distributed in the image information to be processed according to unit distance information; The step of determining the target image detection result information based on the second target distance information includes: Determine the coordinate information corresponding to each of the second objects in the image information to be processed; Based on the coordinate information corresponding to each of the second object representations and the unit distance information, the third target distance information between each of the second object representations and the first object representation is determined; Based on the second target distance information and the third target distance information, the target image detection result information is determined.

7. The method according to claim 1, characterized in that, The step of performing image analysis processing on the image information to be processed to obtain target image detection result information includes: Obtain a target detection instruction, wherein the target detection instruction indicates that a target region in the image information to be processed is to be detected; According to the target detection instruction, the target region in the image information to be processed is detected and processed to obtain target image detection result information.

8. A system, characterized in that, The system includes: The acquisition module is used to acquire information about the image to be processed. The detection module is used to perform image analysis and processing on the image information to be processed, and obtain target image detection result information; Optionally, the acquisition module acquires the image information to be processed, including: Obtain the first image information; The first image information is corrected to obtain the image information to be processed; Optionally, the image information to be processed includes several object representations; The detection module performs image analysis processing on the image information to be processed, and obtains target image detection result information, including: Perform contour recognition processing on each of the object representations in the image information to be processed to obtain the contour representation corresponding to each object representation; Based on the contour representation, position recognition processing is performed on each of the object representations to obtain the position information of each of the object representations; Based on the location information, the target image detection result information is determined; Optionally, each of the object representations includes a first object representation and a plurality of second object representations, wherein the first object representation represents the image information of the image center region in the image information to be processed; The detection module determines the target image detection result information based on the location information, including: Based on the location information of the first object representation and the location information of several second object representations, determine the second target distance information between each second object representation and the first object representation; Based on the second target distance information, the target image detection result information is determined; Optionally, the detection module determines second target distance information between each of the second object representations and the first object representation, including: Determine the first target distance information between each of the second object representations and the first object representation; Based on the first target distance information, several representations of the second object are determined into a set of several object representations; The first target distance information of the second object representation in each of the object representation sets is averaged to obtain the second target distance information of the second object representation in each of the object representation sets; Optionally, each of the second object representations is distributed in the image information to be processed according to unit distance information; The detection module determines the target image detection result information based on the second target distance information, including: Determine the coordinate information corresponding to each of the second objects in the image information to be processed; Based on the coordinate information corresponding to each of the second object representations and the unit distance information, the third target distance information between each of the second object representations and the first object representation is determined; Based on the second target distance information and the third target distance information, the target image detection result information is determined; Optionally, the detection module performs image analysis processing on the image information to be processed to obtain target image detection result information, including: Obtain a target detection instruction, wherein the target detection instruction indicates that a target region in the image information to be processed is to be detected; According to the target detection instruction, the target region in the image information to be processed is detected and processed to obtain target image detection result information.

9. A terminal device, characterized in that, The terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to perform the steps of the method according to any one of claims 1 to 7.

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