CN114626090A - Image data processing method, device and vehicle - Google Patents

Abstract

Embodiments of the present disclosure disclose an image data processing method, device, and vehicle, wherein the processing method includes: performing image restoration on an original image to obtain a three-channel image; if in an image desensitization processing mode, performing image restoration on the three-channel image Perform image recognition on the image to determine a sensitive image area in the three-channel image; determine a mask image of the three-channel image based on the sensitive image area; perform data processing on the three-channel image based on the mask image Desensitization treatment. The embodiments of the present disclosure can effectively protect the security of private data in images collected by sensors.

Description

Image data processing method, device and vehicle

technical field

The present disclosure relates to computer vision technology, and in particular to an image data processing method, device and vehicle.

Background technique

With the development of intelligent identification technology, the issue of data privacy has gradually attracted widespread attention. After the image data is collected by the image collection device, how to ensure the security of the private data in the collected data is an urgent problem to be solved.

SUMMARY OF THE INVENTION

In order to solve the above-mentioned technical problems, the present disclosure is made. Embodiments of the present disclosure provide an image data processing method, apparatus, and vehicle.

According to a first aspect of the embodiments of the present disclosure, a method for processing image data is provided, including:

Perform image restoration on the original image to obtain a three-channel image;

If in the image desensitization processing mode, image recognition is performed on the three-channel image to determine the sensitive image area in the three-channel image;

determining a mask image of the three-channel image based on the sensitive image region;

Based on the mask image, data desensitization processing is performed on the three-channel image.

According to a second aspect of the embodiments of the present disclosure, an apparatus for processing image data is provided, including:

The image restoration module is used to restore the original image to obtain a three-channel image;

A sensitive image area determination module, configured to perform image recognition on the three-channel image and determine a sensitive image area in the three-channel image if it is in the normal image processing mode;

a mask image determination module, configured to determine a mask image of the three-channel image based on the sensitive image area;

A desensitization module, configured to perform data desensitization processing on the three-channel image based on the mask image.

According to a third aspect of the embodiments of the present disclosure, there is provided a vehicle including the image data processing apparatus described in the second aspect.

According to a fourth aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium, where the storage medium stores a computer program, and the computer program is used to execute the image data processing method described in the first aspect.

According to a fifth aspect of the embodiments of the present disclosure, there is provided an electronic device, the electronic device comprising:

processor;

a memory for storing the processor-executable instructions;

The processor is configured to read the executable instructions from the memory and execute the instructions to implement the image data processing method described in the first aspect above.

Based on the image data processing method, device and vehicle provided by the above embodiments of the present disclosure, after image restoration is performed on the original image to obtain a three-channel image, if it is in the image desensitization processing mode, image recognition is performed on the three-channel image to determine the three-channel image. For the sensitive image area in the channel image, the mask image of the three-channel image is determined based on the sensitive image area, and then based on the mask image, data desensitization processing is performed on the three-channel image, which can effectively protect the security of the private data in the image collected by the sensor. .

The technical solutions of the present disclosure will be further described in detail below through the accompanying drawings and embodiments.

Description of drawings

The above and other objects, features and advantages of the present disclosure will become more apparent from the more detailed description of the embodiments of the present disclosure in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present disclosure, and constitute a part of the specification, and are used to explain the present disclosure together with the embodiments of the present disclosure, and do not limit the present disclosure. In the drawings, the same reference numbers generally refer to the same components or steps.

1 is a schematic flowchart of a method for processing image data in an embodiment of the present disclosure;

2 is a schematic flowchart of step S3 in an embodiment of the present disclosure;

3 is a schematic diagram of generating a mask image based on a three-channel image in an example of the present disclosure;

4 is a structural block diagram of an apparatus for processing image data in an embodiment of the present disclosure;

5 is a structural block diagram of a mask image determination module 300 in an embodiment of the present disclosure;

FIG. 6 is a structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure.

Detailed ways

Hereinafter, exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present disclosure, not all of the embodiments of the present disclosure, and it should be understood that the present disclosure is not limited by the example embodiments described herein.

It should be noted that the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

Those skilled in the art can understand that terms such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different steps, devices, or modules, etc., and neither represent any specific technical meaning, nor represent any difference between them. the necessary logical order of .

It should also be understood that, in the embodiments of the present disclosure, "a plurality" may refer to two or more, and "at least one" may refer to one, two or more.

It should also be understood that any component, data or structure mentioned in the embodiments of the present disclosure can generally be understood as one or more in the case of no explicit definition or contrary indications given in the context.

In addition, the term "and/or" in the present disclosure is only an association relationship to describe associated objects, indicating that there can be three kinds of relationships, for example, A and/or B, it can mean that A exists alone, and A and B exist at the same time , there are three cases of B alone. In addition, the character "/" in the present disclosure generally indicates that the related objects are an "or" relationship.

It should also be understood that the description of the various embodiments in the present disclosure emphasizes the differences between the various embodiments, and the same or similar points can be referred to each other, and for the sake of brevity, they will not be repeated.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application or uses in any way.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification.

It should be noted that like numerals and letters refer to like items in the following figures, so once an item is defined in one figure, it does not require further discussion in subsequent figures.

Embodiments of the present disclosure can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with numerous other general-purpose or special-purpose computing system environments or configurations. Examples of well-known terminal equipment, computing systems, environments and/or configurations suitable for use with terminal equipment, computer systems, servers, etc. electronic equipment include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients computer, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments including any of the foregoing, among others.

Electronic devices such as terminal devices, computer systems, servers, etc., may be described in the general context of computer system-executable instructions, such as program modules, being executed by the computer system. Generally, program modules may include routines, programs, object programs, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. Computer systems/servers may be implemented in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located on local or remote computing system storage media including storage devices.

SUMMARY OF THE DISCLOSURE

In the process of realizing the present disclosure, the inventors found that a three-channel image can be obtained by performing image restoration on a native image (ie, a RAW image) collected by an image sensor, and then the three-channel image can be stored. When the three-channel image includes a person image, the person behavior recognition can be performed based on the stored three-channel image.

Since the three-channel image may include sensitive information such as face information, and the sensitive information is not desensitized in the related art, but the three-channel image with the sensitive information is directly stored, there is a risk of sensitive information leakage.

Exemplary overview

In the present disclosure, after image restoration is performed on the original image to obtain the three-channel image, the image area of the sensitive information in the three-channel image, that is, the sensitive image area, can be determined through image recognition technology. Choose a mask image that matches the size of the three-channel image, and set the numerical mask in the corresponding position of the sensitive image area in the mask image. Based on the position of the numerical mask in the mask image, the sensitive image area in the three-channel image can be desensitized accurately, and then the desensitized three-channel image can be saved, so that when performing character behavior recognition, the Three-channel image after desensitization. Through the present disclosure, the desensitized three-channel image can be stored, so that the security of sensitive information can be effectively included.

Exemplary method

FIG. 1 is a schematic flowchart of a method for processing image data in an embodiment of the present disclosure. This embodiment can be applied to an electronic device, as shown in FIG. 1 , including the following steps:

S1: Perform image restoration on the original image to obtain a three-channel image.

The native image is acquired by the image sensor. Exemplarily, the original image may be a raw image (RAW Image Format) of the driver without image restoration, which is collected by an image sensor in the vehicle when the assisted driving function or automatic driving function of the vehicle is turned on, or a raw image of the driver without image restoration. Raw image (RAW Image Format) containing passenger information collected by the image sensor in the car without image restoration.

After the native image is acquired, the native image can be restored by the vehicle control system or a terminal (such as a mobile phone or a server) that can be connected to control the vehicle to obtain a three-channel image corresponding to the native image. The three-channel image may be an RGB three-channel image or a YUV three-channel image.

An R channel image, a G channel image and a B channel image of the same size as the original image may be obtained by, but not limited to, bicubic interpolation.

The specific process of obtaining the R channel image by interpolation of the original image includes: interpolating the R channel pixel value corresponding to the B channel position on the original image, and interpolating the R channel pixel value corresponding to the G channel position on the original image.

The specific process of interpolating the G channel image from the original image includes: interpolating the G channel pixel value corresponding to the B channel position on the original image, and interpolating the G channel pixel value corresponding to the R channel position on the original image.

The specific process of interpolating the B channel image from the original image includes: interpolating the B channel pixel value corresponding to the R channel position on the original image, and interpolating the B channel pixel value corresponding to the G channel position on the original image.

After completing the above interpolation steps, the R channel image, the G channel image and the B channel image with the same image size as the original image can be obtained.

S2: If in the image desensitization processing mode, perform image recognition on the three-channel image to determine the sensitive image area in the three-channel image.

In this embodiment, the image processing mode includes a normal image processing mode and an image desensitization processing mode.

In the normal image processing mode, the three-channel image can be directly stored, or the three-channel image can be directly analyzed and processed.

In the image desensitization processing mode, image recognition can be performed on the three-channel image through a pre-trained image recognition model, and sensitive image areas in the three-channel image can be determined by means of detection frames, such as the driver's face image area, or image area of the passenger's face, etc. The image recognition model may be obtained by training based on the sample image and the recognition result label of the sample image.

S3: Determine the mask image of the three-channel image based on the sensitive image area.

Use a mask image of the same size as the three-channel image. The three-channel image and the mask image can be divided into multiple image blocks, for example, according to the same row and column division method, the three-channel image and the mask image are divided into (N*M) number of image blocks, where N and M are all natural numbers greater than 0.

In the mask image, the image block in the area corresponding to the sensitive image area in the three-channel image is set as the preset mask value, so that when a certain image block of the mask image is read, it is set as the preset mask When the value is set, it can be known that the corresponding image block in the three-channel image is in the sensitive image area.

S4: Based on the mask image, perform data desensitization processing on the three-channel image.

When desensitizing a three-channel image, first obtain all the image blocks in the mask image that are set as preset mask values, and then desensitize all the corresponding image blocks in the obtained three-channel image through the preset data. way to desensitize data.

In this embodiment, after image restoration is performed on the original image to obtain a three-channel image, if it is in the image desensitization processing mode, image recognition is performed on the three-channel image, so as to determine the sensitive image area in the three-channel image, based on the sensitive image area Determining the mask image of the three-channel image, and then performing data desensitization processing on the three-channel image based on the mask image, can effectively protect the security of private data in the image collected by the sensor.

FIG. 2 is a schematic flowchart of step S3 in an embodiment of the present disclosure. As shown in Figure 2, step S3 includes:

S3-1: Based on the position of the sensitive image area in the three-channel image, determine a first image area in the mask image corresponding to the position of the sensitive image area.

FIG. 3 is a schematic diagram of generating a mask image based on a three-channel image in an example of the present disclosure. As shown in Figure 3, a mask image with the same size as the three-channel image is used to divide the three-channel image and the mask image into multiple image blocks. It should be noted that, although the three-channel image and the mask image are divided into 64 image blocks in 8 rows and 8 columns in this example, this embodiment does not limit the specific division method of image blocks, nor does it limit the specific division of image blocks. quantity.

Based on the position of the sensitive image area in the three-channel image, the first image area in the mask image corresponding to the position of the sensitive image area is determined, that is, the image area with a mask value of 1 in the mask image.

S3-2: Determine the remaining image area in the mask image from which the first image area is removed as the second image area, that is, the image area with the mask value of 0 in the mask image.

S3-3: Set a first value for each unit image block in the first image area.

A unit image block is set with a first value, indicating that the unit image block corresponding to its position in the three-channel image is within the sensitive image area. In the example shown in FIG. 3 , the first value is one.

In addition, a second value may also be set for each unit image block in the second image area. A second value is set for a certain unit image block, which indicates that the unit image block corresponding to its position in the three-channel image is outside the sensitive image area. In the example shown in FIG. 3 , the second value is 0.

Correspondingly, step S4 may include: based on the first value, determining the desensitized image area corresponding to the position of the first image area in the three-channel image, that is, according to the positions of all image blocks with a mask value of 1 in the mask image, obtaining All image blocks corresponding to their positions in the three-channel image of the obtained image are determined, and then the desensitized image area that needs to be desensitized is determined; based on the desensitized image area, data desensitization processing is performed on the three-channel image.

In this embodiment, after the three-channel image and the mask image are divided into multiple image blocks, a first value or a second value is set for different image blocks of the mask image, and different values can be used to represent the needs of the three-channel image. Image blocks for which data desensitization is processed, and image blocks that do not require data desensitization. When performing data desensitization on a three-channel image, the image blocks that need to be desensitized in the three-channel image can be quickly determined according to the first value in the mask image, so as to perform data desensitization according to the determined need. The image blocks are desensitized.

In an embodiment of the present disclosure, the step of performing data desensitization processing on the three-channel image based on the desensitized image area may include: adjusting pixel points in the desensitized image area in the three-channel image to target pixel values. The difference between the target pixel value and the pixel boundary value is within a preset difference value range, for example, the preset difference value range may be [0, 5].

In an example of the present disclosure, the pixel value in the three-channel image ranges from 0 to 255, the preset difference range is [0, 5], and a pixel value is selected as the target pixel value within the preset interpolation range. The pixels located in the desensitized image area in the three-channel image are adjusted to the target pixel value, which realizes the hiding of sensitive information and improves the security of sensitive information.

In this embodiment, by setting all pixel values in the desensitized image area where sensitive information exists in the three-channel image as the target pixel value, the sensitive information can be hidden and the security of the sensitive information can be improved.

In another embodiment of the present disclosure, the step of performing data desensitization processing on the three-channel image based on the desensitized image area may include: performing image blurring processing on image blocks located in the desensitized image area in the three-channel image.

Sensitive areas of three-channel images can be blurred by preset convolution filters. The size of the preset convolution kernel and the weight distribution of the convolution kernel may be determined based on the density type of the target object corresponding to the image desensitization area. If the density type identification of the target object indicates that the target object (such as the driver's eye area in the original image) needs to be highly confidential, the resolution of the convolution kernel can be set to be larger, and the weight of the convolution kernel needs to be set to have a higher degree of secrecy. High blur intensity, for example, the size of the preset convolution kernel can be 21x21, so as to ensure that the target object in the sensitive area can be avoided to be recovered later; area), the resolution of the convolution kernel can be set smaller, and the weight of the convolution kernel needs to be set to have a relatively weak blur strength. For example, the size of the preset convolution kernel can be 5x5, Image blurring with a smaller size of the convolution kernel can reduce the complexity of image blurring processing and greatly improve the efficiency of image blurring processing.

In this embodiment, by performing image blurring processing on image blocks located in the desensitized image area in the three-channel image, user privacy can be effectively protected.

In an embodiment of the present disclosure, after step S1, the method may further include: performing behavior detection on the target object in the three-channel image after data desensitization processing, to obtain a behavior detection result of the target object.

In one example of the present disclosure, when the three-channel image includes the target object, the sensitive image region may include the eye region of the target object. After data desensitization processing, the eye region of the target object in the three-channel image can be hidden. Using the desensitized three-channel image, the smoking behavior detection of the target object can be performed for the target object's mouth image area and hand image area, and the behavior detection of whether the hand is holding the steering wheel can also be performed for the target object's hand image area. The behavior detection of whether to wear a seat belt can also be performed for the upper body image area of the target object.

In this embodiment, the behavior detection of the target object in the desensitized three-channel image can ensure the security of private data without affecting the behavior detection based on the image, thereby improving user satisfaction.

In another embodiment of the present disclosure, after step S1, the method may further include: if in the normal image processing mode, performing behavior detection on the target object in the three-channel image to obtain the behavior detection result of the target object.

In an example of the present disclosure, when the three-channel image includes the target object, the smoking behavior detection can be performed on the target object for the mouth image area and the hand image area of the target object, and whether the target object is detected in the hand image area can also be detected. The behavior detection of holding the steering wheel can also be used to detect whether the seat belt is fastened for the upper body image area of the target object.

In this embodiment, when the image is in the normal processing mode, it is not necessary to perform data desensitization processing on the three-channel image, and the behavior detection of the target object in the three-channel image can be performed directly, which improves the selection diversity of whether to perform image desensitization processing. .

Any of the image data processing methods provided in the embodiments of the present disclosure may be executed by any appropriate device with data processing capabilities, including but not limited to: terminal devices and servers. Alternatively, any of the image data processing methods provided in the embodiments of the present disclosure may be executed by a processor, for example, the processor executes any of the image data processing methods mentioned in the embodiments of the present disclosure by invoking corresponding instructions stored in the memory. No further description will be given below.

Exemplary device

FIG. 4 is a structural block diagram of an apparatus for processing image data in an embodiment of the present disclosure. As shown in Figure 4, the image data processing device includes:

The image restoration module 100 is used for image restoration of the original image to obtain a three-channel image;

A sensitive image area determination module 200, configured to perform image recognition on the three-channel image and determine a sensitive image area in the three-channel image if it is in a normal image processing mode;

a mask image determination module 300, configured to determine a mask image of the three-channel image based on the sensitive image area;

The desensitization module 400 is configured to perform data desensitization processing on the three-channel image based on the mask image.

FIG. 5 is a structural block diagram of a mask image determination module 300 in an embodiment of the present disclosure. As shown in Figure 5, the image data processing device includes:

a first image area unit 310, configured to determine a first image area corresponding to the position of the sensitive image area in the mask image based on the position of the sensitive image area in the three-channel image;

The second image area unit 320 is configured to determine the remaining image area in the mask image after removing the first image area as the second image area;

a numerical value setting unit 330, configured to set a first numerical value for each unit image block in the first image area;

Wherein, the desensitization module 400 is configured to determine, based on the first value, a desensitized image area corresponding to the position of the first image area in the three-channel image, and based on the desensitized image area, desensitize the three-channel image Perform data desensitization.

In an embodiment of the present disclosure, the desensitization module 400 is configured to adjust the pixel points located in the desensitized image area in the three-channel image to a target pixel value, wherein the target pixel value and the pixel boundary value The difference is within the preset difference range.

In another embodiment of the present disclosure, the desensitization module 400 is configured to perform image blurring processing on image blocks located in the desensitized image area in the three-channel image.

In an embodiment of the present disclosure, the apparatus for processing image data may further include:

The first behavior detection module is configured to perform behavior detection on the target object in the three-channel image after data desensitization processing, and obtain the behavior detection result of the target object.

In another embodiment of the present disclosure, the apparatus for processing image data may further include:

The second behavior detection module is configured to perform behavior detection on the target object in the three-channel image if it is in the normal image processing mode, and obtain the behavior detection result of the target object.

It should be noted that the specific implementation of the image data processing apparatus in the embodiment of the present disclosure is similar to the specific implementation of the image data processing method in the embodiment of the present disclosure. For details, please refer to the image data processing method section. In order to reduce redundancy, I won't go into details.

Exemplary Electronics

Hereinafter, an electronic device according to an embodiment of the present disclosure will be described with reference to FIG. 6 . As shown in FIG. 6 , the electronic device includes one or more processors 10 and memory 20 .

The processor 10 may be a central processing unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

Memory 20 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and/or cache memory, or the like. The non-volatile memory may include, for example, read only memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the image data processing methods and/or the various embodiments of the present disclosure described above. or other desired functionality. Various contents such as input signals, signal components, noise components, etc. may also be stored in the computer-readable storage medium.

In one example, the electronic device may also include an input device 30 and an output device 40 interconnected by a bus system and/or other form of connection mechanism (not shown). The input device 30 may be, for example, a keyboard, a mouse, or the like. Output devices 40 may include, for example, displays, speakers, printers, and communication networks and their connected remote output devices, among others.

For simplicity, only some of the components of the electronic device related to the present disclosure are shown in FIG. 6 , and components such as buses, input/output interfaces, and the like are omitted. In addition to this, the electronic device may also include any other appropriate components depending on the specific application.

Exemplary computer-readable storage medium

A computer-readable storage medium can employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses or devices, or a combination of any of the above. More specific examples (non-exhaustive list) of readable storage media include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

The basic principles of the present disclosure have been described above with reference to specific embodiments. However, it should be pointed out that the advantages, advantages, effects, etc. mentioned in the present disclosure are only examples rather than limitations, and these advantages, advantages, effects, etc. should not be considered to be A must-have for each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of example and easy understanding, but not for limitation, and the above details do not limit the present disclosure to be implemented by using the above specific details.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments may be referred to each other. As for the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for related parts, please refer to the partial description of the method embodiment.

The block diagrams of devices, apparatuses, apparatuses, and systems referred to in this disclosure are merely illustrative examples and are not intended to require or imply that the connections, arrangements, or configurations must be in the manner shown in the block diagrams. As those skilled in the art will appreciate, these means, apparatuses, apparatuses, systems may be connected, arranged, configured in any manner. Words such as "including", "including", "having" and the like are open-ended words meaning "including but not limited to" and are used interchangeably therewith. As used herein, the words "or" and "and" refer to and are used interchangeably with the word "and/or" unless the context clearly dictates otherwise. As used herein, the word "such as" refers to and is used interchangeably with the phrase "such as but not limited to".

The methods and apparatus of the present disclosure may be implemented in many ways. For example, the methods and apparatus of the present disclosure may be implemented in software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present disclosure can also be implemented as programs recorded in a recording medium, the programs including machine-readable instructions for implementing methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

It should also be noted that, in the apparatus, device and method of the present disclosure, each component or each step may be decomposed and/or recombined. These disaggregations and/or recombinations should be considered equivalents of the present disclosure.

The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The foregoing description has been presented for the purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the present disclosure to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims (10)

1. A method of processing image data, comprising:

carrying out image restoration on the original image to obtain a three-channel image;

if the three-channel image is in an image desensitization processing mode, performing image recognition on the three-channel image, and determining a sensitive image area in the three-channel image;

determining a mask image of the three-channel image based on the sensitive image area;

and performing data desensitization processing on the three-channel image based on the mask image.

2. The method of claim 1, wherein the determining a mask image for the three channel image based on the sensitive image region comprises:

determining a first image area corresponding to the position of the sensitive image area in the mask image based on the position of the sensitive image area in the three-channel image;

determining a residual image area of the mask image, from which the first image area is removed, as a second image area;

setting a first numerical value for each unit image block in the first image area;

wherein the performing data desensitization processing on the three-channel image based on the mask image comprises:

determining a desensitization image region corresponding to the first image region position in the three-channel image based on the first numerical value;

and carrying out data desensitization processing on the three-channel image based on the desensitization image area.

3. The method of claim 2, wherein the performing data desensitization processing on the three-channel image based on the desensitization image region comprises:

and adjusting pixel points in the desensitization image region in the three-channel image into target pixel values, wherein the difference value between the target pixel values and the pixel boundary values is within a preset difference value range.

4. The method of claim 2, wherein the performing data desensitization processing on the three-channel image based on the desensitization image region comprises:

and carrying out image blurring processing on the image block positioned in the desensitization image area in the three-channel image.

5. The method according to any one of claims 1-4, further comprising, after said performing data desensitization processing on said three channel image based on said mask image:

and performing behavior detection on the target object in the three-channel image after data desensitization processing to obtain a behavior detection result of the target object.

6. The method according to any one of claims 1-4, further comprising, after said image restoration of the native image to obtain a three-channel image:

and if the three-channel image is in the normal image processing mode, performing behavior detection on the target object in the three-channel image to obtain a behavior detection result of the target object.

7. An apparatus for processing image data, comprising:

the image restoration module is used for carrying out image restoration on the original image to obtain a three-channel image;

the sensitive image area determining module is used for carrying out image recognition on the three-channel image and determining a sensitive image area in the three-channel image if the three-channel image is in an image normal processing mode;

the mask image determining module is used for determining a mask image of the three-channel image based on the sensitive image area;

and the desensitization module is used for performing data desensitization processing on the three-channel image based on the mask image.

8. A vehicle comprising the image data processing apparatus of claim 7.

9. A computer-readable storage medium storing a computer program for executing the image data processing method according to any one of claims 1 to 6.

10. An electronic device, the electronic device comprising:

a processor;

a memory for storing the processor-executable instructions;

the processor is used for reading the executable instructions from the memory and executing the instructions to realize the image data processing method of any one of the claims 1 to 6.

Images