CN118212668A

CN118212668A - Target identification method, device, electronic equipment and medium

Info

Publication number: CN118212668A
Application number: CN202211615713.XA
Authority: CN
Inventors: 汪辉
Original assignee: Zhejiang Uniview Technologies Co Ltd
Current assignee: Zhejiang Uniview Technologies Co Ltd
Priority date: 2022-12-15
Filing date: 2022-12-15
Publication date: 2024-06-18

Abstract

The embodiment of the application discloses a target identification method, a target identification device, electronic equipment and a medium. Wherein the method comprises the following steps: determining image acquisition parameters for image acquisition of a target to be identified and/or image processing parameters for processing the image of the target to be identified according to the image parameters of the candidate image of the candidate target; the candidate images are images stored in a target information base; image acquisition is carried out on the target to be identified based on the image acquisition parameters, and/or the image of the target to be identified is processed based on the image processing parameters, so that an image to be identified is obtained; and matching the image to be identified with the candidate image, and determining a target image matched with the image to be identified from the candidate image. According to the technical scheme, the image parameters of the sample image are counted, and the image acquisition and/or the image correction are carried out on the target to be identified according to the counting result of the image parameters, so that the problem of low target identification accuracy is solved.

Description

Target identification method, device, electronic equipment and medium

Technical Field

The present application relates to the field of machine vision, and in particular, to a method, an apparatus, an electronic device, and a medium for identifying a target.

Background

Object recognition, such as face recognition, vehicle recognition, etc., is a popular application in the field of artificial intelligence. At present, in the target recognition scheme, an image to be recognized is generally compared with images in a pre-established sample library, and a recognition result of the image to be recognized is determined according to a sample image with highest similarity in the comparison result.

However, due to the different imaging environments, there is often a certain difference between the image parameters of the image to be identified, such as image brightness, image contrast, etc., and the image parameters of the sample image. The image parameter difference becomes a main factor affecting the accuracy of target identification, and how to reduce the image parameter difference and improve the accuracy of target identification becomes a problem to be solved.

Disclosure of Invention

The application provides a target identification method, a device, electronic equipment and a medium, which are used for carrying out image acquisition and/or image correction on a target to be identified according to the statistical result of image parameters by carrying out statistics on the image parameters of a sample image so as to solve the problem of low target identification accuracy.

According to an aspect of the present application, there is provided a target recognition method, the method comprising:

Determining image acquisition parameters for image acquisition of a target to be identified and/or image processing parameters for processing the image of the target to be identified according to the image parameters of the candidate image of the candidate target; the candidate images are images stored in a target information base;

Image acquisition is carried out on the target to be identified based on the image acquisition parameters, and/or the image of the target to be identified is processed based on the image processing parameters, so that an image to be identified is obtained;

And matching the image to be identified with the candidate image, and determining a target image matched with the image to be identified from the candidate image.

According to another aspect of the present application, there is provided an object recognition apparatus including:

The image parameter determining module is used for determining image acquisition parameters for image acquisition of the target to be identified and/or image processing parameters for processing the image of the target to be identified according to the image parameters of the candidate image of the candidate target; the candidate images are images stored in a target information base;

The image generation module to be identified is used for carrying out image acquisition on the target to be identified based on the image acquisition parameters and/or processing the image of the target to be identified based on the image processing parameters to obtain an image to be identified;

and the target image determining module is used for matching the image to be identified with the candidate image and determining a target image matched with the image to be identified from the candidate image.

According to another aspect of the present application, there is provided an object recognition electronic device including:

at least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the object recognition method according to any one of the embodiments of the present application.

According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the object recognition method according to any one of the embodiments of the present application.

According to the technical scheme of the embodiment of the application, according to the image parameters of the candidate images of the candidate targets, the image acquisition parameters for carrying out image acquisition on the targets to be identified and/or the image processing parameters for processing the images of the targets to be identified are determined; image acquisition is carried out on the target to be identified based on the image acquisition parameters, and/or the image of the target to be identified is processed based on the image processing parameters, so that an image to be identified is obtained; and matching the image to be identified with the candidate image, and determining a target image matched with the image to be identified from the candidate image. According to the technical scheme, the image parameters of the sample image are counted, and the image acquisition and/or the image correction are carried out on the target to be identified according to the counting result of the image parameters, so that the problem of low target identification accuracy is solved.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for identifying an object according to a first embodiment of the present application;

FIG. 2 is a flow chart of a target recognition method according to a second embodiment of the present application;

Fig. 3 is a schematic structural diagram of a target recognition device according to a third embodiment of the present application;

fig. 4 is a schematic structural diagram of an electronic device implementing a target recognition method according to an embodiment of the present application.

Detailed Description

In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," "target," and the like in the description and claims of the present application and in the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a target recognition method according to an embodiment of the present application, where the method may be applied to a scene of recognition of a target such as a face, a vehicle, etc., and the method may be performed by a target recognition device, where the target recognition device may be implemented in hardware and/or software, and the target recognition device may be configured in an electronic device having a target recognition capability. As shown in fig. 1, the method includes:

S110, determining image acquisition parameters for image acquisition of the target to be identified and/or image processing parameters for processing the image of the target to be identified according to the image parameters of the candidate image of the candidate target.

The scheme can be executed by visual processing equipment with the functions of target detection, recognition and the like, wherein the visual processing equipment can comprise image acquisition equipment such as a camera, an infrared imager and the like, and also can comprise image processing equipment such as a computer, a server and the like. The image processing device may acquire a stored image in a pre-established target information base, determine a candidate image of a candidate target in the target information base according to an application scene, for example, in a traffic scene, the candidate target may be a target such as a vehicle or a pedestrian, and in a monitoring scene, the candidate target may be a target such as a face or a human body. The candidate objects may be one or more, the candidate images may be images containing candidate objects, and one or more candidate images may be present for each candidate object. The target to be identified can be a target in an image acquired by the image acquisition equipment, the specific information of the target to be identified is unknown, and the target to be identified needs to be matched with the candidate target so as to determine which target to be identified is, and further the specific information is determined.

The image processing device may determine image parameters of each candidate image, where the image parameters may include an image brightness value, an image contrast, a number of image noise points, an image color difference value, an image sharpness, and the like. Specifically, the image processing apparatus may determine an image brightness value according to image acquisition parameters such as a shutter, a gain, and a diaphragm of the image acquisition apparatus, or may determine image parameters such as an image brightness value, an image contrast, and the like by reading each pixel value of the image. Image parameters such as image brightness value, image contrast, image noise number, image color difference value, image definition and the like can also be determined by the neural network model. For example, an image is predetermined as a training sample, at least one of image parameters such as an image brightness value, an image contrast, an image noise number, an image color difference value, and an image definition in the training sample is determined, and the neural network model is trained according to the training sample and the image parameters to obtain an image parameter determination model. The candidate image is input into an image parameter determination model, and the image parameters of the candidate image are determined.

The image processing device may count image parameters of the candidate image, and determine image acquisition parameters and/or image processing parameters of the object to be identified according to the result of the statistics of the image parameters. Wherein the target to be identified may be one of the candidate targets or may not belong to the candidate targets.

Specifically, the image processing device may determine a statistical result of image acquisition parameters such as a shutter, a gain, and an aperture according to the statistical result of the image brightness value, and determine an image acquisition parameter of the object to be identified according to the statistical result of the image acquisition parameter. The image acquisition parameters may include one or more sets. The image processing device may also determine the image processing parameters of the object to be identified according to the statistical results of the image parameters such as image contrast, image noise number, image color difference value, image sharpness, and the like. The statistical result may include the results of the maximum value, the average value, the variance, the parameter distribution, and the like. The image processing parameters may include contrast adjustment parameters, noise reduction parameters, color difference adjustment parameters, sharpness adjustment parameters, and the like. The image processing parameters may include one or more sets.

S120, performing image acquisition on the target to be identified based on the image acquisition parameters, and/or processing the image of the target to be identified based on the image processing parameters to obtain the image to be identified.

In order to avoid the target recognition errors caused by differences between the image to be recognized and the candidate image in terms of image brightness value, image contrast, image noise number, image color difference value, image definition and the like. The image processing device can control the image acquisition device to reduce the difference between the image of the object to be identified and the candidate image in the image acquisition stage, can adjust the image of the object to be identified according to the image processing parameters after the image of the object to be identified is obtained, and can adjust the imaging of the object to be identified through the image acquisition parameters in the image acquisition stage and adjust the image of the object to be identified in the image processing stage.

The brightness value of the image can be adjusted in the image acquisition stage, and the adjustment can be realized through image processing after the image of the object to be identified is obtained. The image contrast, the number of image noise points, the image color difference value, the image definition and the like can be used for carrying out image processing on the image of the target to be identified through the image processing parameters so as to reduce the difference between the image contrast and the candidate image. For example, the image processing device may perform image acquisition on the target to be identified according to image acquisition parameters such as a shutter and a gain, and may adjust the contrast of the image of the target to be identified according to the contrast adjustment parameter after obtaining the image of the target to be identified, and perform operations such as noise reduction on the image of the target to be identified according to the noise reduction parameter. The image processing device may use the image of the object to be identified acquired by using the image acquisition parameters and/or the image of the object to be identified processed by using the image processing parameters as the image to be identified.

And S130, matching the image to be identified with the candidate image, and determining a target image matched with the image to be identified from the candidate image.

The image processing device may compare the image to be identified with the candidate images, and calculate the similarity between each image to be identified and each candidate image. Specifically, the image processing device may sequentially select one image to be identified and one candidate image from among the images to be identified and the candidate images to form an image pair, and calculate the similarity between the image to be identified and the candidate image in each image pair. And taking the candidate image in the image pair with the highest similarity as a target image of the image to be identified. Based on the target image, the image processing apparatus can determine the recognition result of the image to be recognized. Specifically, the image processing apparatus may use the target to which the target image is matched as the recognition result of the image to be recognized. For example, the image to be identified may include a face image a, a face image B, and a face image C, the candidate image includes a face image D and a face image E, and the face image E is a target image if the similarity between the face image a and the face image E is the highest by calculating the similarity between the image to be identified and the candidate image, and the identification result of the image to be identified may be a face corresponding to the face image E.

In one possible solution, determining, according to image parameters of candidate images of candidate objects, image acquisition parameters for image acquisition of the object to be identified and/or image processing parameters for processing the image of the object to be identified, includes:

Determining a minimum value and a maximum value in image parameters of the candidate image;

Selecting reference image parameters from the minimum value to the maximum value based on a preset parameter interval;

And determining image acquisition parameters for image acquisition of the target to be identified and/or image processing parameters for processing the image of the target to be identified according to the reference image parameters.

In this scheme, the image processing apparatus may count the maximum value and the minimum value of the image parameters of the candidate image, and set a series of reference image parameters between the minimum value and the maximum value at preset parameter intervals. Wherein the preset parameter interval may be a fixed value, e.g. 15. For example, the maximum value of the image parameter is 100, the minimum value is 10, the preset parameter interval is 15, and the reference image parameters may include 10, 25, 40, 55, 70, 85, and 100. The preset parameter interval may also be a value based on a statistical distribution change of the image parameters, for example, the image parameters are in the range of [30,60], the parameter interval is set to 5, the image parameters are in the ranges of [10,30 ] and (60, 100], and the parameter interval is set to 20. The reference image parameters may include 10,30, 35, 40, 45, 50, 55, 60, 80, and 100. Based on the respective reference image parameters, the image processing device may determine image acquisition parameters of the object to be identified and/or image processing parameters of the image of the object to be identified.

According to the method and the device, the reference image parameters with different gradients can be set in the image parameter range of the candidate image, the candidate image which is most similar to the imaging environment of the target to be identified is determined based on the reference image parameters with multiple gradients, and therefore accuracy and reliability of target identification are improved.

In another possible solution, determining, according to image parameters of candidate images of candidate objects, image acquisition parameters for image acquisition of the object to be identified and/or image processing parameters for processing the image of the object to be identified, includes:

Taking the image parameters of each candidate image as reference image parameters;

It will be appreciated that the image processing apparatus may directly take the image processing parameters of each candidate image as reference image parameters and determine the image acquisition parameters of the object to be identified and/or the image processing parameters of the image of the object to be identified based on the reference image parameters.

The method and the device can directly utilize the image parameters of the candidate images, are favorable for reducing the imaging gap between the candidate images and the images to be identified, and further realize the accuracy and the robustness of target identification.

Optionally, determining, according to the reference image parameter, an image acquisition parameter for image acquisition of the object to be identified and/or an image processing parameter for processing the image of the object to be identified, including:

If the reference image parameter is a reference brightness value, determining an image acquisition parameter corresponding to the brightness value, and taking the brightness value of an image obtained by image acquisition of the target to be identified based on the image acquisition parameter as the reference brightness value;

And if the reference image parameter is at least one of a reference brightness value, a reference contrast value, a reference noise number, a reference image color difference value and a reference image definition, determining an image processing parameter corresponding to the reference image parameter, and taking the image parameter obtained after the image of the object to be identified is processed based on the image processing parameter as the reference image parameter.

It is easy to understand that the image brightness value depends on the image acquisition parameters of the image acquisition apparatus and the ambient brightness where the shooting target is located. Thus, if the reference image parameter is a reference luminance value, the image processing apparatus may calculate a corresponding image acquisition parameter from each reference luminance value. For example, if the image parameter is an image luminance value, the image processing apparatus may calculate the corresponding image acquisition parameter based on the association relationship of the image luminance value and the image acquisition parameter according to each reference luminance value. The image acquisition parameters may be acquisition parameter sets consisting of values of shutter, gain and aperture. The reference luminance values 10, 25, 40, 55, 70, 85, and 100 may correspond to the acquisition parameter sets A, B, C, D, E, F and G. The image acquisition device acquires images of the target to be identified based on the image acquisition parameters, wherein the images are more similar to the brightness values of the candidate images.

The image processing apparatus may further adjust the image quality of the object to be identified by means of image processing after obtaining the image of the object to be identified, so as to obtain an image to be identified that approximates the image quality of the candidate image. If the image parameter is one or more of a reference brightness value, a reference contrast value, a reference noise number, a reference image color difference value and a reference image sharpness, the image processing device may calculate a corresponding image processing parameter according to each reference image parameter. Corresponding contrast adjustment parameters A, B, C, D, E, F and G are determined, for example, from the reference contrast values 10, 25, 40, 55, 70, 85 and 100.

According to the scheme, the imaging of the target to be identified is controlled in two stages of image acquisition and image processing through the image acquisition parameters and the image processing parameters, so that the image to be identified, which is close to the imaging environment of the candidate image, is obtained, and the accuracy rate and the anti-interference capability of target identification are improved.

On the basis of the above scheme, optionally, the reference image parameter includes a first preset number of reference brightness values and a second preset number of reference contrast values; the first preset number of reference brightness values corresponds to the first preset number of image acquisition parameters, and the second preset number of reference contrast values corresponds to the second preset number of image processing parameters;

Image acquisition is carried out on the target to be identified based on the image acquisition parameters, and/or the image of the target to be identified is processed based on the image processing parameters, so that an image to be identified is obtained, and the method comprises the following steps:

Respectively carrying out image acquisition on the target to be identified based on a first preset number of image acquisition parameters to obtain a first preset number of acquisition images;

For each acquired image in the first preset number of acquired images, respectively performing image processing on the acquired images based on the second preset number of image processing parameters to obtain a second preset number of processed images;

Processing images obtained by processing the first preset number of acquired images are used as images to be identified; the number of the images to be identified is the product of the first preset number and the second preset number.

In an embodiment, the image capturing device may capture a first preset number of captured images according to a first preset number of image capturing parameters. After obtaining the acquired images, the image processing apparatus may perform corresponding image processing on each of the acquired images based on the second preset number of image processing parameters. And taking each processing image as an image to be identified. Let the first preset number be M, the second preset number be N, and the number of images to be identified be mxn. The image acquisition device can compare M multiplied by N images to be identified with the candidate images, and compared with a single image of the target to be identified, the imaging of the target to be identified with different image parameter gradients can increase the probability of matching with the candidate images.

According to the scheme, the image to be identified is increased, so that the matching probability of the imaging of the object to be identified and the candidate image is improved, and the reliability of object identification is guaranteed.

Example two

Fig. 2 is a flowchart of a target recognition method according to a second embodiment of the present application, which is optimized based on the foregoing embodiment. As shown in fig. 2, the method of this embodiment specifically includes the following steps:

s210, acquiring an image of the object to be identified to obtain an initial image, and determining the similarity between the initial image and a stored image in a database.

In the scheme, the image acquisition equipment can acquire the image of the target to be identified based on preset image acquisition parameters to obtain an initial image. The image processing device may perform pre-matching on the initial image in the target information base, and calculate a similarity between the initial image and the image stored in the target information base. The similarity can be determined based on a histogram distance, a cosine distance and the like, can be determined based on hash algorithm calculation, can be further determined based on convolutional neural network feature extraction, and is determined according to cosine similarity of extracted feature vectors.

S220, selecting a preset number of stored images from the stored images to serve as candidate images according to the similarity sorting, or taking the stored images with the similarity larger than a preset similarity threshold value as candidate images.

The image processing apparatus may sort the respective similarity calculation results in a preset order. The preset sequence may be a high-to-low sequence or a low-to-high sequence. The preset number and the preset similarity threshold can be adaptively determined according to actual conditions. According to the similarity sorting, the image processing apparatus may select a preset number of stored images from among the stored images as candidate images, for example, the preset order is from high to low, the preset number is 3, and the image processing apparatus may sort the respective similarity calculation results in order from high to low, and may select the first 3 stored images in the similarity sorting as candidate images. The image processing apparatus may also determine the candidate images according to the similarity magnitude, for example, a preset order from high to low, a preset similarity threshold value of 80%, and stored images having a similarity greater than 80% of the preset similarity threshold value as the candidate images.

S230, judging whether the number of the candidate images is larger than a number threshold. If yes, S240 is executed, and if no, S270 is executed.

The image processing apparatus may determine a selection manner of the reference image parameters according to the number of candidate images. If the number of candidate images is greater than the number threshold, which indicates that the number of candidate images is too large, the image processing apparatus may perform S240-S250 to obtain a proper amount of reference image parameters and simultaneously ensure a matching probability of the candidate images and the images to be identified in order to improve the processing efficiency. If the number of candidate images is less than or equal to the number threshold, the image processing apparatus may perform S260 to directly take the image parameters of the respective candidate images as reference image parameters.

S240, determining the minimum value and the maximum value in the image parameters of the candidate image.

S250, selecting a reference image parameter from the minimum value to the maximum value based on a preset parameter interval.

For example, assuming that the number of candidate images is P and the number threshold is 10, if P is greater than 10, the image parameter of the candidate image is one of the image brightness value, the image contrast, the image noise number, the image color difference value and the image sharpness, the maximum value of the image parameter in the P candidate images is determined to be 100, and the minimum value is determined to be 10. If the preset parameter interval is 15, seven reference image parameters of 10, 25, 40, 55, 70, 85 and 100 can be obtained. If P is greater than 10, the image parameters of the candidate image are at least two of an image brightness value, an image contrast, an image noise number, an image color difference value and an image definition, taking the image parameters as two examples, determining that the maximum value of a first image parameter in the image parameters of the P candidate images is 100, the minimum value is 10, and a preset parameter interval is 15, so that seven first reference image parameters of 10, 25, 40, 55, 70, 85 and 100 can be obtained, and for a second image parameter, determining that the maximum value of the second image parameter is 70, the minimum value is 20, and the preset parameter interval is 10, six second reference image parameters of 20, 30, 40, 50, 60 and 70 can be obtained, and combining the first reference image parameter and the second reference image parameter to determine 7*6 =42 groups of reference image parameters altogether. The same applies to the case where the image parameters are two or more.

And S260, taking the image parameters of each candidate image as reference image parameters.

Taking the data in S250 as an example, if P is less than or equal to 10, the image parameters of the P candidate images are taken as reference image parameters, so as to obtain P reference image parameters.

S270, determining image acquisition parameters for image acquisition of the target to be identified and/or image processing parameters for processing the image of the target to be identified according to the reference image parameters.

After determining the reference image parameters, the image processing device may determine corresponding image acquisition parameters and/or image processing parameters according to each reference image parameter, so as to perform image acquisition on the object to be identified under different image acquisition parameters and/or perform image processing on the image of the object to be identified under different image processing parameters.

S280, performing image acquisition on the target to be identified based on the image acquisition parameters, and/or processing the image of the target to be identified based on the image processing parameters to obtain the image to be identified.

And S290, matching the image to be identified with the candidate image, and determining a target image matched with the image to be identified from the candidate image.

Example III

Fig. 3 is a schematic structural diagram of a target recognition device according to a third embodiment of the present application, where the device may execute the target recognition method according to any embodiment of the present application, and the device has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 3, the apparatus includes:

An image parameter determining module 310, configured to determine, according to image parameters of candidate images of candidate objects, image acquisition parameters for image acquisition of the objects to be identified and/or image processing parameters for processing the images of the objects to be identified; the candidate images are images stored in a target information base;

The image to be identified generating module 320 is configured to perform image acquisition on the target to be identified based on the image acquisition parameters, and/or process an image of the target to be identified based on the image processing parameters, so as to obtain an image to be identified;

and the target image determining module 330 is configured to match the image to be identified with the candidate image, and determine a target image matched with the image to be identified from the candidate images.

In one possible implementation, the image parameter determining module 310 includes:

A maximum value determining unit configured to determine a minimum value and a maximum value among image parameters of the candidate image;

The first reference parameter determining unit is used for selecting reference image parameters from the minimum value to the maximum value based on a preset parameter interval;

And the first image parameter determining unit is used for determining image acquisition parameters for image acquisition of the target to be identified and/or image processing parameters for processing the image of the target to be identified according to the reference image parameters.

In another possible implementation, the image parameter determining module 310 includes:

a second reference parameter determination unit configured to use the image parameters of the candidate images as reference image parameters;

And the second image parameter determining unit is used for determining image acquisition parameters for image acquisition of the target to be identified and/or image processing parameters for processing the image of the target to be identified according to the reference image parameters.

In this aspect, optionally, the apparatus further includes:

The similarity determining module is used for acquiring the image of the object to be identified to obtain an initial image and determining the similarity between the initial image and a stored image in a database;

And the candidate image determining module is used for selecting a preset number of stored images from the stored images to serve as candidate images according to the similarity sequence, or taking the stored images with the similarity larger than a preset similarity threshold value with the initial image as the candidate images.

In this embodiment, optionally, the image parameter determining module 310 is further configured to:

If the number of the candidate images is larger than a number threshold, determining the minimum value and the maximum value in the image parameters of the candidate images;

determining image acquisition parameters for image acquisition of the target to be identified and/or image processing parameters for processing the image of the target to be identified according to the reference image parameters;

If the number of the candidate images is smaller than or equal to a number threshold, taking the image parameters of each candidate image as reference image parameters;

Optionally, the image parameter determining module 310 is specifically configured to:

The image to be identified generating module 320 includes:

The acquisition image generation unit is used for respectively carrying out image acquisition on the target to be identified based on a first preset number of image acquisition parameters to obtain a first preset number of acquisition images;

The processing image generation unit is used for respectively carrying out image processing on the acquired images based on the second preset number of image processing parameters for each acquired image in the first preset number of acquired images to obtain a second preset number of processing images;

the image generation unit to be identified is used for taking the processed images obtained by image processing of the first preset number of acquired images as images to be identified; the number of the images to be identified is the product of the first preset number and the second preset number.

The object recognition device provided by the embodiment of the application can execute the object recognition method provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.

Example IV

Fig. 4 shows a schematic diagram of an electronic device 410 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 4, the electronic device 410 includes at least one processor 411, and a memory, such as a Read Only Memory (ROM) 412, a Random Access Memory (RAM) 413, etc., communicatively connected to the at least one processor 411, wherein the memory stores computer programs executable by the at least one processor, and the processor 411 may perform various suitable actions and processes according to the computer programs stored in the Read Only Memory (ROM) 412 or the computer programs loaded from the storage unit 418 into the Random Access Memory (RAM) 413. In the RAM413, various programs and data required for the operation of the electronic device 410 may also be stored. The processor 411, the ROM 412, and the RAM413 are connected to each other through a bus 414. An input/output (I/O) interface 415 is also connected to bus 414.

Various components in the electronic device 410 are connected to the I/O interface 415, including: an input unit 416 such as a keyboard, a mouse, etc.; an output unit 417 such as various types of displays, speakers, and the like; a storage unit 418, such as a magnetic disk, optical disk, or the like; and a communication unit 419 such as a network card, modem, wireless communication transceiver, etc. The communication unit 419 allows the electronic device 410 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

The processor 411 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 411 performs the various methods and processes described above, such as the target recognition method.

In some embodiments, the object recognition method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 418. In some embodiments, some or all of the computer program may be loaded and/or installed onto the electronic device 410 via the ROM 412 and/or the communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the object recognition method described above may be performed. Alternatively, in other embodiments, the processor 411 may be configured to perform the target recognition method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A method of target identification, the method comprising:

2. Method according to claim 1, characterized in that determining image acquisition parameters for image acquisition of an object to be identified and/or image processing parameters for processing an image of the object to be identified from image parameters of a candidate image of a candidate object comprises:

3. Method according to claim 1, characterized in that determining image acquisition parameters for image acquisition of an object to be identified and/or image processing parameters for processing an image of the object to be identified from image parameters of a candidate image of a candidate object comprises:

4. Method according to claim 1, characterized in that determining image acquisition parameters for image acquisition of an object to be identified and/or image processing parameters for processing an image of the object to be identified from image parameters of a candidate image of a candidate object comprises:

5. Method according to any of claims 2-4, characterized in that determining image acquisition parameters for image acquisition of an object to be identified and/or image processing parameters for processing an image of the object to be identified from the reference image parameters comprises:

6. The method of claim 5, wherein the reference image parameters include a first preset number of reference luminance values and a second preset number of reference contrast values; the first preset number of reference brightness values corresponds to the first preset number of image acquisition parameters, and the second preset number of reference contrast values corresponds to the second preset number of image processing parameters;

7. The method according to claim 1, wherein before determining the image acquisition parameters for image acquisition of the object to be identified and/or the image processing parameters for processing the image of the object to be identified from the image parameters of the candidate image of the candidate object, the method further comprises:

Acquiring an image of the object to be identified to obtain an initial image, and determining the similarity between the initial image and a stored image in a database;

And selecting a preset number of stored images from the stored images as candidate images according to the similarity sorting, or taking the stored images with the similarity larger than a preset similarity threshold value with the initial image as candidate images.

8. An object recognition apparatus, characterized in that the apparatus comprises:

9. An electronic device, the electronic device comprising:

at least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the object recognition method of any one of claims 1-7.

10. A computer readable storage medium storing computer instructions for causing a processor to perform the object recognition method of any one of claims 1-7.