CN111524085A

CN111524085A - Adaptive image processing method, adaptive image processing device, electronic equipment and storage medium

Info

Publication number: CN111524085A
Application number: CN202010624491.2A
Authority: CN
Inventors: 蒋梦; 张怀; 钟雯清; 王鹤; 高玉亮; 高飞; 董建华; 丁海友; 康秋静; 黄玉君
Original assignee: Beijing Dacheng Guoce Vision Technology Co ltd; Beijing Dacheng Guoce Science And Technology Co ltd
Current assignee: Beijing Dacheng Guoce Technology Co ltd; Beijing Dacheng Guoce Vision Technology Co ltd
Priority date: 2020-07-02
Filing date: 2020-07-02
Publication date: 2020-08-11
Anticipated expiration: 2040-07-02
Also published as: CN111524085B

Abstract

A self-adaptive image processing method, a device and an electronic device belong to the technical field of data processing, and the method comprises the following steps: setting image acquisition equipment for acquiring an image containing a track at preset positions on two sides of the track by taking an extension line of the track as a track visual field; adopting a preset filter, and generating a second image sequence based on filtering calculation of pixel values of an Mth image and an M-1 th image in the N images; based on the average color mean value of the images in the second image sequence, performing enhancement operation on pixels in the images in the second image sequence to obtain an enhanced third image sequence; and extracting the characteristics of the third image sequence to obtain a characteristic matrix set corresponding to the image sequence, and judging whether foreign matters exist in the track visual field. Through the treatment scheme of the present disclosure, the invaded foreign body can be effectively detected and monitored.

Description

Adaptive image processing method, adaptive image processing device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to an adaptive image processing method and apparatus, an electronic device, and a storage medium.

Background

As an important transportation mode, rail transit generally has a large number of geographical environments, which are greatly different, especially in some areas with severe environments, and the foreign matters intruding into the boundary of rail transit lines mainly include: the collapsed big trees and the strong wind form a sand-going path on the track, the debris flow is accumulated on the track, and the rocks roll down on the mountain.

The traditional mode is through artifical mode of patrolling and examining the railway going on, but the artifical mode of patrolling and examining consumes more manpower resources on the one hand, also can lead to efficiency lower simultaneously, has influenced the timeliness and the cost that the railway foreign matter detected. Still there is the scheme of patrolling and examining the railway through the camera among the prior art, but through the mode that the camera was made a video recording, still need the manual work to discern the image of making a video recording of acquireing, because the difference of artificial experience can lead to the efficiency of image identification to be lower.

In the process of image recognition based on the image collected by the camera, the quality of the shot image can not meet the requirement usually because the shot image is influenced by light and environment, and the subsequent judgment of foreign matter detection based on the image is influenced.

Disclosure of Invention

In view of the above, embodiments of the present disclosure provide an adaptive image processing method, an adaptive image processing apparatus, an electronic device, and a storage apparatus, so as to at least partially solve the problems in the prior art.

In a first aspect, an embodiment of the present disclosure provides an adaptive image processing method, including:

setting image acquisition equipment for acquiring an image containing the track at preset positions on two sides of the track by taking an extension line of the track as a track visual field, wherein the image acquisition equipment is used for acquiring a first image sequence containing N images in the track visual field within a preset time period, and N is greater than 1;

adopting a preset filter, and generating a second image sequence based on filtering calculation of pixel values of an Mth image and an M-1 th image in N images, wherein N is greater than or equal to M, and M is greater than 1;

based on the average color mean value of the images in the second image sequence, performing enhancement operation on pixels in the images in the second image sequence to obtain an enhanced third image sequence;

and extracting the characteristics of the third image sequence to obtain a characteristic matrix set corresponding to the image sequence, and transmitting the characteristic matrix set to a network server in communication connection with the image acquisition equipment, so that the network server can judge whether foreign matters exist in the track view according to the characteristic matrix set.

According to a specific implementation manner of the embodiment of the present disclosure, the generating a second image sequence based on performing filtering calculation on pixel values of an mth image and an M-1 th image of the N images by using a preset filter includes:

setting a filter matrix, and performing mean value filter calculation on the Mth image and the (M-1) th image through the filter matrix;

and taking the image after the filtering calculation as the image in the second image sequence.

According to a specific implementation manner of the embodiment of the present disclosure, the taking the image after the filtering calculation as the image in the second image sequence includes:

performing N-1 times of filtering calculation on N images in the first image sequence to obtain N-1 filtering calculation results;

and setting the filtered images obtained by the N-1 filtering results as the images in the second image sequence.

According to a specific implementation manner of the embodiment of the present disclosure, the performing an enhancement operation on pixels in the images in the second image sequence based on the average color mean of the images in the second image sequence includes:

defining a non-linear enhancement function, comparing image pixel values in a second image sequence with the average color mean;

and increasing the pixel value of which the image color value is greater than the average color mean value, and decreasing the pixel value of which the image color value is less than the average color mean value.

According to a specific implementation manner of the embodiment of the present disclosure, acquiring a first image sequence including N images in a track view within a preset time period includes:

acquiring a preset image sampling frequency and the starting time and the ending time of a preset time period;

acquiring images in a time period formed by the starting time and the ending time based on the image sampling frequency;

and forming a first image sequence in the orbit visual field in a preset time period based on the images acquired in the time period formed by the starting time and the ending time.

According to a specific implementation manner of the embodiment of the present disclosure, the forming an image sequence in a capture orbit view in a preset time period based on an image captured in a time period formed by a start time and an end time includes:

judging whether the similarity between the currently acquired image and the previous image is greater than a preset value or not;

and if so, not saving the currently acquired image in the first image sequence.

According to a specific implementation manner of the embodiment of the present disclosure, the determining, by the network server, whether a foreign object exists in the track view according to the feature matrix set includes:

setting a convolutional layer in the network server so as to facilitate the characteristic acquisition of images based on the convolutional layer;

and setting a full connection layer in the network server so as to classify the characteristics of the acquired images based on the full connection layer.

carrying out classification calculation on the numerical values in the characteristic matrix set to obtain a classification estimated value;

based on the classification estimated value, judging the classification of the object in the acquired image containing the track image to obtain a classification result;

judging whether the classification result is a classification contained in a known classification;

if not, the object in the acquired image including the track image is judged to be an intruding foreign object.

In a second aspect, an embodiment of the present disclosure provides an adaptive image processing apparatus, including:

the acquisition module is used for setting and acquiring image acquisition equipment comprising track images at preset positions on two sides of a track by taking an extension line of the track as a track visual field, and acquiring a first image sequence comprising N images in the track visual field within a preset time period, wherein N is greater than 1;

the filtering module is used for generating a second image sequence by adopting a preset filter and based on filtering calculation of pixel values of an Mth image and an M-1 image in N images, wherein N is greater than or equal to M, and M is greater than 1;

the enhancement module is used for executing enhancement operation on pixels in the images in the second image sequence based on the average color mean value of the images in the second image sequence to obtain an enhanced third image sequence;

and the execution module is used for extracting the features of the third image sequence to obtain a feature matrix set corresponding to the image sequence and transmitting the feature matrix set to a network server in communication connection with the image acquisition equipment, so that the network server can judge whether foreign matters exist in the track view according to the feature matrix set.

In a third aspect, an embodiment of the present disclosure further provides an electronic device, where the electronic device includes:

at least one processor; and the number of the first and second groups,

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the adaptive image processing method of the first aspect or any implementation manner of the first aspect.

In a fourth aspect, the disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the adaptive image processing method in the first aspect or any implementation manner of the first aspect.

In a fifth aspect, the disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the adaptive image processing method of the first aspect or any implementation manner of the first aspect.

The self-adaptive image processing scheme in the embodiment of the disclosure comprises that image acquisition equipment for acquiring an image containing a track is arranged at preset positions on two sides of the track by taking an extension line of the track as a track visual field, and is used for acquiring a first image sequence containing N images in the track visual field within a preset time period, wherein N is greater than 1; adopting a preset filter, and generating a second image sequence based on filtering calculation of pixel values of an Mth image and an M-1 th image in N images, wherein N is greater than or equal to M, and M is greater than 1; based on the average color mean value of the images in the second image sequence, performing enhancement operation on pixels in the images in the second image sequence to obtain an enhanced third image sequence; and extracting the characteristics of the third image sequence to obtain a characteristic matrix set corresponding to the image sequence, and transmitting the characteristic matrix set to a network server in communication connection with the image acquisition equipment, so that the network server can judge whether foreign matters exist in the track view according to the characteristic matrix set. By the processing scheme, the efficiency of the self-adaptive image processing is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in 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 disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a flow chart of an adaptive image processing method of the present invention;

FIG. 2 is a flow chart of another adaptive image processing method of the present invention;

FIG. 3 is a flow chart of another adaptive image processing method of the present invention;

FIG. 4 is a flow chart of another adaptive image processing method of the present invention;

FIG. 5 is a schematic structural diagram of an adaptive image processing apparatus according to the present invention;

fig. 6 is a schematic diagram of an electronic device according to the present invention.

Detailed Description

The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.

It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.

It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.

In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.

The embodiment of the disclosure provides a self-adaptive image processing method. The adaptive image processing method provided by the present embodiment may be executed by a computing device, which may be implemented as software, or implemented as a combination of software and hardware, and may be integrally provided in a server, a client, or the like.

Referring to fig. 1, an adaptive image processing method in an embodiment of the present disclosure may include the following steps:

s101, at preset positions on two sides of a track, setting and collecting image collecting equipment containing track images by taking an extension line of the track as a track view field, wherein the image collecting equipment is used for collecting a first image sequence containing N images in the track view field within a preset time period, and N is larger than 1.

In order to determine whether foreign objects invade in and beside the track, an image capturing device (e.g., a camera) for capturing images of the track may be disposed beside the track so as to capture N images of the track within a preset time according to a set sampling period, thereby forming a first image sequence consisting of N images.

In the process of forming the first image sequence, the preset image sampling frequency and the starting time and the ending time of the preset time period can be obtained; acquiring images in a time period formed by the starting time and the ending time based on the image sampling frequency; and forming a first image sequence in the orbit visual field in a preset time period based on the images acquired in the time period formed by the starting time and the ending time.

As an embodiment, in the process of forming an image sequence in the acquisition track view within a preset time period, it may be determined whether the similarity between the currently acquired image and the previous image is greater than a preset value; if yes, the currently acquired image is not stored in the first image sequence, so that the difference of the images in the first image sequence is improved.

S102, a preset filter is adopted, filtering calculation is carried out on pixel values of an Mth image and an M-1 th image in N images, and a second image sequence is generated, wherein N is larger than or equal to M, and M is larger than 1.

In order to improve the smooth transition of the images in the first image sequence, a filter can be arranged, and the images in the N first image sequences are subjected to filtering processing through the filter to form a second image sequence consisting of N-1 images.

Specifically, a filter matrix may be set, and mean value filtering calculation may be performed on the mth image and the M-1 th image by the filter matrix; and taking the image after the filtering calculation as the image in the second image sequence.

As an embodiment, N-1 filtering computations may be performed on N images in the first image sequence to obtain N-1 filtering computation results; and setting the filtered images obtained by the N-1 filtering results as the images in the second image sequence.

S103, based on the average color mean value of the images in the second image sequence, enhancing the pixels in the images in the second image sequence to obtain an enhanced third image sequence.

Besides improving the difference of the images, further, the contrast of the images is improved, for this purpose, a non-linear enhancement function may be defined, and the image pixel values in the second image sequence are compared with the average color mean value; and increasing the pixel value of which the image color value is greater than the average color mean value, and decreasing the pixel value of which the image color value is less than the average color mean value to obtain a third image sequence. The images in the third image sequence meet the requirements of smooth transition and color difference.

And S104, performing feature extraction on the third image sequence to obtain a feature matrix set corresponding to the image sequence, and transmitting the feature matrix set to a network server in communication connection with the image acquisition equipment, so that the network server can judge whether foreign matters exist in the track view according to the feature matrix set.

After the image is processed in the image acquisition device, if the processed image is directly transmitted to a server (a network server), the system bandwidth is increased, and the system resource consumption is increased.

The network server is provided with a convolution layer so as to facilitate the characteristic collection of images based on the convolution layer, and the network server is provided with a full connection layer so as to facilitate the characteristic classification of the collected images based on the full connection layer.

Judging whether foreign matters exist in the track visual field at the network server according to the characteristic matrix set, wherein the judging step comprises the following steps: carrying out classification calculation on the numerical values in the characteristic matrix set to obtain a classification estimated value; based on the classification estimated value, judging the classification of the object in the acquired image containing the track image to obtain a classification result; judging whether the classification result is a classification contained in a known classification; if not, the object in the acquired image including the track image is judged to be an intruding foreign object.

Referring to fig. 2, according to a specific implementation manner of the embodiment of the present disclosure, the performing filtering calculation based on pixel values of an M-th image and an M-1-th image of N images by using a preset filter includes:

s201, setting a filter matrix, and performing mean value filter calculation on the Mth image and the (M-1) th image through the filter matrix;

and S202, taking the image after the filtering calculation as the image in the second image sequence.

Referring to fig. 3, according to a specific implementation manner of the embodiment of the present disclosure, the taking the image after the filtering calculation as the image in the second image sequence includes:

s301, performing N-1 times of filtering calculation on N images in the first image sequence to obtain N-1 filtering calculation results;

s302, setting the filtered images obtained by the N-1 filtering results as the images in the second image sequence.

Referring to fig. 4, according to a specific implementation manner of the embodiment of the present disclosure, the performing an enhancement operation on pixels in the images in the second image sequence based on the average color mean of the images in the second image sequence includes:

s401, defining a nonlinear enhancement function, and comparing image pixel values in a second image sequence with the average color mean value;

s402, increasing the pixel value of which the image color value is larger than the average color mean value, and decreasing the pixel value of which the image color value is smaller than the average color mean value.

According to a specific implementation manner of the embodiment of the present disclosure, acquiring a first image sequence including N images in a track view within a preset time period includes: acquiring a preset image sampling frequency and the starting time and the ending time of a preset time period; acquiring images in a time period formed by the starting time and the ending time based on the image sampling frequency; and forming a first image sequence in the orbit visual field in a preset time period based on the images acquired in the time period formed by the starting time and the ending time.

According to a specific implementation manner of the embodiment of the present disclosure, the forming an image sequence in a capture orbit view in a preset time period based on an image captured in a time period formed by a start time and an end time includes: judging whether the similarity between the currently acquired image and the previous image is greater than a preset value or not; and if so, not saving the currently acquired image in the first image sequence.

According to a specific implementation manner of the embodiment of the present disclosure, the determining, by the network server, whether a foreign object exists in the track view according to the feature matrix set includes: setting a convolutional layer in the network server so as to facilitate the characteristic acquisition of images based on the convolutional layer; and setting a full connection layer in the network server so as to classify the characteristics of the acquired images based on the full connection layer.

According to a specific implementation manner of the embodiment of the present disclosure, the determining, by the network server, whether a foreign object exists in the track view according to the feature matrix set includes: carrying out classification calculation on the numerical values in the characteristic matrix set to obtain a classification estimated value; based on the classification estimated value, judging the classification of the object in the acquired image containing the track image to obtain a classification result; judging whether the classification result is a classification contained in a known classification; if not, the object in the acquired image including the track image is judged to be an intruding foreign object.

Corresponding to the above embodiment, referring to fig. 5, the disclosed embodiment further provides an adaptive image processing apparatus 50, including:

the acquisition module 501 is configured to set, at a preset position on both sides of a track, an image acquisition device for acquiring an image including the track, with an extension line of the track as a track view field, so as to acquire a first image sequence including N images in the track view field within a preset time period, where N is greater than 1;

a filtering module 502, configured to generate a second image sequence based on filtering computations on pixel values of an mth image and an M-1 st image of the N images by using a preset filter, where N is greater than or equal to M, and M is greater than 1;

an enhancement module 503, configured to perform an enhancement operation on pixels in the images in the second image sequence based on the average color mean of the images in the second image sequence, so as to obtain an enhanced third image sequence;

an executing module 504, configured to perform feature extraction on the third image sequence to obtain a feature matrix set corresponding to the image sequence, and transmit the feature matrix set to a network server in communication connection with the image acquisition device, so that the network server determines, according to the feature matrix set, whether a foreign object exists in the track view.

For parts not described in detail in this embodiment, reference is made to the contents described in the above method embodiments, which are not described again here.

Referring to fig. 6, an embodiment of the present disclosure also provides an electronic device 60, including:

at least one processor; and the number of the first and second groups,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the adaptive image processing method of the foregoing method embodiments.

The disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the adaptive image processing method in the aforementioned method embodiments.

The disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the adaptive image processing method in the aforementioned method embodiments.

Referring now to FIG. 6, a schematic diagram of an electronic device 60 suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 6, the electronic device 60 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 60 are also stored. The processing device 601, the ROM602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 60 to communicate with other devices wirelessly or by wire to exchange data. While the figures illustrate an electronic device 60 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of the embodiments of the present disclosure.

It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising the at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects the internet protocol addresses from the at least two internet protocol addresses and returns the internet protocol addresses; receiving an internet protocol address returned by the node evaluation equipment; wherein the obtained internet protocol address indicates an edge node in the content distribution network.

Alternatively, the computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from the at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first retrieving unit may also be described as a "unit for retrieving at least two internet protocol addresses".

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.

The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. An adaptive image processing method, comprising:

2. The method according to claim 1, wherein generating the second image sequence based on filtering computation of pixel values of an mth image and an M-1 image of the N images by using a preset filter comprises:

3. The method according to claim 2, wherein said filtering the image after calculation as the image in the second image sequence comprises:

4. The method of claim 1, wherein performing enhancement operations on pixels in the images in the second sequence of images based on the average color mean of the images in the second sequence of images comprises:

5. The method of claim 1, wherein acquiring a first sequence of images comprising N images within an orbital field of view within a preset time period comprises:

6. The method according to claim 5, wherein the forming a sequence of images within a field of view of the acquisition orbit within a preset time period based on the images acquired within the time period consisting of the start time and the end time comprises:

and if so, not saving the currently acquired image in the first image sequence.

7. The method of claim 1, wherein the network server determines whether a foreign object exists in the track view according to the feature matrix set, comprising:

8. The method of claim 1, wherein the network server determines whether a foreign object exists in the track view according to the feature matrix set, comprising:

9. An adaptive image processing apparatus, comprising:

10. An electronic device, characterized in that the electronic device comprises:

at least one processor; and the number of the first and second groups,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the adaptive image processing method of any of the preceding claims 1-8.

11. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the adaptive image processing method of any one of the preceding claims 1-8.