Target feature random error analysis system and method based on cloud computing

Abstract

The invention discloses a target feature random error analysis system and method based on cloud computing, and relates to the technical field of image processing. The flight state of an unmanned aerial vehicle is recorded, the flight state of the unmanned aerial vehicle corresponds to the motion trail of the same object in a picture in an airborne camera, and a picture transfer database is obtained through collection; the method comprises the following steps: capturing a video clip with a non-random error in a picture from a shooting record, fitting a function of the non-random error picture feature changing along with time, extracting a picture frame from the picture shot by an airborne camera, extracting an image area with abnormal contrast change in the video frame, obtaining a record of a real-time flight state of the unmanned aerial vehicle, and recording the real-time flight state of the unmanned aerial vehicle. The method comprises the following steps: respectively acquiring image slices of two image areas according to the area of a shooting object in the image areas in a shooting picture, and judging whether random interference exists in the picture or not from two aspects of change characteristics of interference on the picture and comparison with a monitoring model.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a target feature random error analysis system and method based on cloud computing. Background Art

[0002] When drones are shooting images, blurred motion often occurs due to the high-speed flight of the target, resulting in a blur effect in the continuous frame images. The blur effect is produced in the continuous frame images. The acquisition, storage, and transmission of drone images will inevitably introduce some noises of varying degrees, such as salt and pepper noise, Gaussian noise, etc., which will cause image errors. Due to the rapid changes and ambiguity of high-speed moving objects, traditional attitude estimation methods often cannot provide accurate results. Therefore, in the existing technology, a large number of images interfered by random errors will be generated in the video images recorded or transmitted back by the drone camera system, which brings difficulties to image processing and image analysis. Summary of the invention

[0003] The purpose of the present invention is to provide a target feature random error analysis system and method based on cloud computing to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solution: a target feature random error analysis method based on cloud computing, the method comprising: Step S100: During the flight of the drone, the flight state of the drone is recorded, and the flight state of the drone is matched with the motion trajectory of the same object in the picture of the onboard camera to obtain a picture transfer database; Step S200: Obtain historical shooting records of the onboard camera, capture video clips with non-random errors in the pictures from the shooting records, fit a function of the picture features of the non-random errors changing over time, and compile them into an error change database; Step S300: extracting a picture frame from the picture taken by the onboard camera, and extracting an image area with abnormal contrast change in the video frame; Step S400: obtaining a record of the real-time flight status of the drone, extracting the area of ​​the photographed object in the image area in the photographed picture one unit time ago, and obtaining image slices of the two image areas respectively; Step S500: comparing the image errors of two image slices, and when the difference between the image error and the non-random error is greater than a threshold, sending the image slices to relevant personnel.

[0005] Furthermore, step S100 includes: Step S101: photographing an object through an onboard camera of a drone, taking the object as a target object, dividing the photographing screen of the onboard camera of the drone into a number of unit areas, establishing a plane coordinate system for the photographing screen, and numbering each unit area according to the coordinates of the unit area; Step S102: obtaining a video record of a target object shot by an onboard camera, and capturing a segment of the video record as a first target video record; Step S103: Obtain the flight attitude and flight speed of the drone when shooting the first target video record and record them in the first state set R1. Obtain the coordinates ref1 of the unit area where the target object is located in the first frame and the corresponding coordinates ref2 in the second frame from two frames of the first target video record. Step S104: In the plane coordinate system, calculate the vector coordinate α of the indication vector, α= ref2- ref1, associate the indication vector with the first state set R1 to form a picture transfer data group, adjust the flight attitude and flight speed of the drone, collect several picture transfer data groups, and obtain a picture transfer database.

[0006] Further, step S200 includes: Step S201: obtaining a video clip with an error picture from a video record shot by an onboard camera, and intercepting a section of the video record as a second target video record; Step S202: Obtain n frames from the second target video record, mark the unit area with picture errors in each frame, collect the unit areas with picture errors in the kth frame to form an error area, the error area is composed of adjacent unit areas, obtain the image of the error area, and record it as the error image im of the kth error area k ; Step S203: Gathering error images of each frame, arranging the errors according to the order of the pictures in the second target video record to obtain an error image sequence, calculating the image quality evaluation index of each error image, and arranging the error images according to the order to obtain an error value sequence; Step S204: Perform function fitting on the image quality evaluation index sequence to obtain a function of the image quality evaluation index changing with time, record the function as an error change function, obtain the flight attitude and flight speed of the drone when shooting the second target video record and record them in the second state set R2, combine the error change function and the second state set R2 into an error change group, collect the corresponding relationship between several groups of error change functions and the second state set R2, collect the error change groups, and obtain an error change database.

[0007] Furthermore, the calculation method of the image quality evaluation index includes: 2-1: Convert the image into a grayscale image, set u grayscale level judgment levels to quantify the grayscale image, and count the occurrence probability of each grayscale level in the grayscale image; 2-2: Get the probability p of the e-th gray level e , calculate the information entropy H of the grayscale image, ; 2-3: Use information entropy as the image quality evaluation indicator of grayscale images.

[0008] Furthermore, step S300 includes: Step S301: from the real-time video captured by the onboard camera of the drone, a first image and a second image are obtained in the order of video capture, and the contrasts of the first image and the second image are calculated, respectively, and are recorded as CR1 and CR2. When CR1>CR2, a contrast threshold M is calculated, where M=CR1-CR2. Step S302: Obtain the image slice corresponding to the jth unit area in the first image, denoted as psc 1j , the image slice corresponding to the jth unit area in the second image is denoted as psc 2j , get psc 1j The contrast is denoted as r 1j ,psc 2j The contrast is denoted as r 2j ; Step S303: When r 1j -r 2j >M, mark the jth unit area in the second image as the first target unit area; When the video image is disturbed, a main feature is the decrease in the contrast of the picture. The change in the overall contrast of the two captured pictures is used as a reference value, and the unit area where the contrast decreases faster than the reference value is compared unit area by unit area. In the video recording, the first image is recorded before the second image, so the areas in the second image where the contrast decreases more sharply may be contaminated by random errors.

[0009] Furthermore, step S400 includes: Step S401: obtaining the timestamps of the first image and the second image captured by the drone's onboard camera, recording the time period from capturing the first image to capturing the second image as the target time period, obtaining the flight attitude and flight speed of the drone in the target time period, and recording them in the target state set RT; Step S402: acquiring a first state set identical to the target state set RT in the picture transfer database, and acquiring a vector coordinate β of an indicator vector associated with the first state set; Step S403: Obtain the coordinates ref of the first target unit area t1 , calculate the coordinates of the second target unit area ref t2 , ref t2 = ref t1 -β, the second target unit area in the first image; Step S404: obtaining an image slice of the first target unit area, denoted as psc1, and an image slice of the second target unit area, denoted as psc2, and obtaining image quality evaluation indicators of psc1 and psc2, denoted as rq1 and rq2 respectively; By tracing the source of the first target area, a corresponding area of ​​the first target area photographed before a unit time is found in the first image, and the first target area is further compared with the second target area to confirm whether the airborne camera is interfered by random errors.

[0010] Furthermore, step S500 includes: Step S501: acquiring a second state set identical to the target state set RT in an error change database, and acquiring an error change function associated with the second state set; Step S502: Mark the data point whose value is equal to the image quality evaluation index rq2 in the error change function, record the data point as the first data point, and obtain the data point of the target time period in the error change function as the second data point; Step S503: obtaining an image quality evaluation index corresponding to the second data point, and using the image quality evaluation index as an error reference value; Step S504: setting an error threshold γ, when |Q-rq1|>γ, marking the image slice psc1 and sending it to the relevant management personnel.

[0011] In order to better implement the above method, a target feature random error analysis system based on cloud computing is also proposed, and the system includes: Furthermore, a picture movement management module, an error management module, a contrast comparison module, an image slice management module and an image judgment module, wherein the picture movement management module is used to manage the relative motion relationship between the object and the viewing angle in the picture taken by the airborne camera under different flight states, the error management module is used to manage the picture characteristics of non-random errors, the contrast comparison module is used to compare the contrast changes in the picture frame and obtain the first target unit area, the image slice management module is used to obtain the aircraft status record of the drone and manage the image slices, and the image judgment module is used to judge whether there are non-random errors in the image slices; Furthermore, the picture movement management module includes: a picture management unit, an indication vector management unit and a picture transfer database management unit, wherein the picture management unit is used to manage the unit management area in the picture taken by the airborne camera, the indication vector management unit is used to mark the relative motion relationship of the object taken in the state of the drone relative to the picture taken, and obtain the indication vector, and the picture transfer database management unit is used to manage the picture transfer database; Further, the error management module includes: an error image management unit, an image quality evaluation unit, a function fitting unit and an error change database management unit, wherein the error image management unit is used to manage the pictures with non-random errors in the historical shooting records, the image quality evaluation unit is used to calculate the image quality evaluation index in the image, the function fitting unit is used to perform function fitting on the image quality evaluation index of the error image to obtain a function of the image quality evaluation index changing with time, and the error change database management unit is used to manage the error change database; Furthermore, the contrast comparison module includes: a global contrast comparison unit and a local contrast comparison unit, wherein the global contrast comparison unit is used to compare the contrast of the complete image to obtain a contrast threshold, and the local contrast comparison unit is used to compare the contrast in a unit area in the image to obtain a first target unit area; Furthermore, the image slice management module includes: a state management unit and an image area backtracking unit, wherein the state management unit is used to obtain the state record of the real-time flight of the UAV, and the image area backtracking unit is used to backtrack the image of the first target unit area to obtain the image of the second target unit area; Furthermore, the image judgment module includes: a reference value management unit, an error judgment unit and an information feedback unit, wherein the reference value management unit is used to obtain an error reference value, the error judgment unit is used to judge the difference between the image quality evaluation index and the error reference value, and the information feedback unit is used to mark the image slices that meet the judgment conditions and send them to relevant management personnel.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: by marking the corresponding relationship between the flight status of the drone and the image movement and image interference, a monitoring model for known non-random error interference is established. The image of the onboard camera is monitored and the image quality is evaluated. Through two comparisons, from the two aspects of the changing characteristics of the image being disturbed and the comparison with the monitoring model, it is judged whether there is random interference in the image, thereby improving the accuracy of the judgment. The image area where random interference may exist is marked to facilitate further processing of the video information. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a schematic diagram of the structure of a target feature random error analysis system based on cloud computing in the present invention; Figure 2 The present invention is a flowchart of a method for analyzing random errors of target features based on cloud computing. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] Example: Figure 1 and Figure 2 As shown, the present invention provides a technical solution, a target feature random error analysis method based on cloud computing: Step S100: During the flight of the drone, the flight state of the drone is recorded, and the flight state of the drone is matched with the motion trajectory of the same object in the picture of the onboard camera to obtain a picture transfer database; Wherein, step S100 includes: Step S101: photographing an object through an onboard camera of a drone, taking the object as a target object, dividing the photographing screen of the onboard camera of the drone into a number of unit areas, establishing a plane coordinate system for the photographing screen, and numbering each unit area according to the coordinates of the unit area; Step S102: obtaining a video record of a target object shot by an onboard camera, and capturing a segment of the video record as a first target video record; Step S103: Obtain the flight attitude and flight speed of the drone when shooting the first target video record and record them in the first state set R1. Obtain the coordinates ref1 of the unit area where the target object is located in the first frame and the corresponding coordinates ref2 in the second frame from two frames of the first target video record. Step S104: in the plane coordinate system, calculate the vector coordinate α of the indication vector, α= ref2- ref1, associate the indication vector with the first state set R1 to form a picture transfer data set, adjust the flight attitude and flight speed of the drone, collect several picture transfer data sets, and obtain a picture transfer database; In the embodiment, in the coordinate system, each unit area is given a unique rectangular coordinate, and the index vector is labeled with a two-dimensional vector, ref2=(5, 5), ref1=(3, 2), so α=(2, 3).

[0016] Step S200: Obtain historical shooting records of the onboard camera, capture video clips with non-random errors in the pictures from the shooting records, fit a function of the picture features of the non-random errors changing over time, and compile them into an error change database; Wherein, step S200 includes: Step S201: obtaining a video clip with an error picture from a video record shot by an onboard camera, and intercepting a section of the video record as a second target video record; Step S202: Obtain n frames from the second target video record, mark the unit area with picture errors in each frame, collect the unit areas with picture errors in the kth frame to form an error area, the error area is composed of adjacent unit areas, obtain the image of the error area, and record it as the error image im of the kth error area k ; Step S203: Gathering error images of each frame, arranging the errors according to the order of the pictures in the second target video record to obtain an error image sequence, calculating the image quality evaluation index of each error image, and arranging the error images according to the order to obtain an error value sequence; Among them, the calculation method of the image quality evaluation index includes: 2-1: Convert the image into a grayscale image, set u grayscale level judgment levels to quantify the grayscale image, and count the occurrence probability of each grayscale level in the grayscale image; 2-2: Get the probability p of the e-th gray level e , calculate the information entropy H of the grayscale image,

[0017] 2-3: Use information entropy as the image quality evaluation indicator of grayscale images.

[0018] Step S204: Perform function fitting on the image quality evaluation index sequence to obtain a function of the image quality evaluation index changing with time, record the function as an error change function, obtain the flight posture and flight speed of the drone when shooting the second target video record and record them in the second state set R2, form an error change group with the error change function and the second state set R2, collect the corresponding relationship between several groups of error change functions and the second state set R2, collect the error change groups, and obtain an error change database.

[0019] Step S300: extracting a picture frame from the picture taken by the onboard camera, and extracting an image area with abnormal contrast change in the video frame; Wherein, step S300 includes: Step S301: from the real-time video captured by the onboard camera of the drone, a first image and a second image are obtained in the order of video capture, and the contrasts of the first image and the second image are calculated, respectively, and are recorded as CR1 and CR2. When CR1>CR2, a contrast threshold M is calculated, where M=CR1-CR2. In an embodiment, a method for calculating the contrast ratio includes: By obtaining the brightness value of each pixel in the image, the brightness value at the image position (x, y) is recorded as I(x, y); Calculate the mean value μ of the brightness in the image, , where N represents the total number of pixels in the x-axis direction of the image, and M represents the total number of pixels in the y-axis direction of the image; Calculate the standard deviation σ of the pixels in the image, ; The standard deviation σ is used as the contrast of the image; Step S302: Obtain the image slice corresponding to the jth unit area in the first image, denoted as psc 1j , the image slice corresponding to the jth unit area in the second image is denoted as psc 2j , get psc 1j The contrast is denoted as r 1j ,psc 2j The contrast is denoted as r 2j ; Step S303: When r 1j -r 2j >M, the j-th unit area in the second image is marked as the first target unit area.

[0020] Step S400: obtaining a record of the real-time flight status of the drone, extracting the area of ​​the photographed object in the image area in the photographed picture one unit time ago, and obtaining image slices of the two image areas respectively; Wherein, step S400 includes: Step S401: obtaining the timestamps of the first image and the second image captured by the drone's onboard camera, recording the time period from capturing the first image to capturing the second image as the target time period, obtaining the flight attitude and flight speed of the drone in the target time period, and recording them in the target state set RT; Step S402: acquiring a first state set identical to the target state set RT in the picture transfer database, and acquiring a vector coordinate β of an indicator vector associated with the first state set; Step S403: Obtain the coordinates ref of the first target unit area t1 , calculate the coordinates of the second target unit area ref t2 , ref t2 = ref t1-β, the second target unit area in the first image; For example, when the coordinates of the first target unit area ref t1 = (7, 5), the indication vector obtained is (2, 3), ref t2 = ref t1 -β=(5,2), take the unit area with coordinates (5,2) in the first image as the second unit area; Step S404: obtaining an image slice of the first target unit area, denoted as psc1, and an image slice of the second target unit area, denoted as psc2, and obtaining image quality evaluation indicators of psc1 and psc2, denoted as rq1 and rq2 respectively.

[0021] Step S500: comparing the image errors of two image slices, and when the difference between the image error and the non-random error is greater than a threshold, sending the image slices to relevant personnel; Wherein, step S500 includes: Step S501: acquiring a second state set identical to the target state set RT in an error change database, and acquiring an error change function associated with the second state set; Step S502: Mark the data point whose value is equal to the image quality evaluation index rq2 in the error change function, record the data point as the first data point, and obtain the data point of the target time period in the error change function as the second data point; Step S503: obtaining an image quality evaluation index corresponding to the second data point, and using the image quality evaluation index as an error reference value; Step S504: setting an error threshold γ, when |Q-rq1|>γ, marking the image slice psc1 and sending it to the relevant management personnel.

[0022] The system includes: a picture movement management module, an error management module, a contrast comparison module, an image slice management module and an image judgment module; Among them, the picture movement management module is used to manage the relative movement relationship between the object and the viewing angle in the picture taken by the onboard camera under different flight states, wherein, the picture movement management module is used to manage the relative movement relationship between the object and the viewing angle in the picture taken by the onboard camera under different flight states, the error management module is used to manage the picture characteristics of non-random errors, the contrast comparison module is used to compare the contrast changes in the picture frame, and obtain the first target unit area, the image slice management module is used to obtain the aircraft status record of the drone, manage the image slices, and the image judgment module is used to judge whether there is a non-random error in the image slice; The error management module is used to manage the picture features of non-random errors, wherein the error management module includes: an error image management unit, an image quality evaluation unit, a function fitting unit and an error change database management unit, wherein the error image management unit is used to manage pictures with non-random errors in historical shooting records, the image quality evaluation unit is used to calculate the image quality evaluation index in the image, the function fitting unit is used to perform function fitting on the image quality evaluation index of the error image to obtain a function of the image quality evaluation index changing over time, and the error change database management unit is used to manage the error change database; The contrast comparison module is used to compare the contrast changes in the picture frame to obtain the first target unit area, wherein the contrast comparison module includes: a global contrast comparison unit and a local contrast comparison unit, wherein the global contrast comparison unit is used to compare the contrast of the complete image to obtain the contrast threshold, and the local contrast comparison unit is used to compare the contrast in the unit area in the image to obtain the first target unit area; The image slice management module is used to obtain the aircraft status record of the UAV and manage the image slices. The image slice management module includes: a status management unit and an image area backtracking unit. The status management unit is used to obtain the real-time flight status record of the UAV. The image area backtracking unit is used to backtrack the image of the first target unit area to obtain the image of the second target unit area. Among them, the image judgment module is used to judge whether there is a non-random error in the image slice, wherein the image judgment module includes: a reference value management unit, an error judgment unit and an information feedback unit, wherein the reference value management unit is used to obtain an error reference value, the error judgment unit is used to judge the difference between the image quality evaluation index and the error reference value, and the information feedback unit is used to mark the image slice that meets the judgment conditions and send it to relevant management personnel.

[0023] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A target feature random error analysis method based on cloud computing, characterized by: Methods include: Step S100: During the flight of the drone, the flight state of the drone is recorded, and the flight state of the drone is matched with the motion trajectory of the same object in the picture of the onboard camera to obtain a picture transfer database; Step S200: Obtain historical shooting records of the onboard camera, capture video clips with non-random errors in the pictures from the shooting records, fit a function of the picture features of the non-random errors changing over time, and compile them into an error change database; Step S300: extracting a picture frame from the picture taken by the onboard camera, and extracting an image area with abnormal contrast change in the video frame; Step S400: obtaining a record of the real-time flight status of the drone, extracting the area of ​​the photographed object in the image area in the photographed picture one unit time ago, and obtaining image slices of the two image areas respectively; Step S500: comparing the image errors of two image slices, and when the difference between the image error and the non-random error is greater than a threshold, sending the image slices to relevant personnel.

2. The target feature random error analysis method based on cloud computing according to claim 1 is characterized by: Step S100 includes: Step S101: photographing an object through an onboard camera of a drone, taking the object as a target object, dividing the photographing picture of the onboard camera of the drone into a plurality of unit areas, establishing a plane coordinate system for the photographing picture, and numbering each unit area according to the coordinates of the unit area; Step S102: obtaining a video record of a target object shot by an onboard camera, and intercepting a segment of the video record as a first target video record; Step S103: Obtain the flight attitude and flight speed of the drone when shooting the first target video record and record them in the first state set R1, wherein the flight attitude of the drone includes: roll, pitch and yaw. From two frames of the first target video record, obtain the coordinates ref1 of the unit area where the target object is located in the first frame and the corresponding coordinates ref2 in the second frame; Step S104: In the plane coordinate system, calculate the vector coordinate α of the indication vector, α= ref2- ref1, associate the indication vector with the first state set R1 to form a picture transfer data group, adjust the flight attitude and flight speed of the drone, collect several picture transfer data groups, and obtain a picture transfer database.

3. The target feature random error analysis method based on cloud computing according to claim 2 is characterized by: Step S200 includes: Step S201: obtaining a video clip with an error picture from a video record shot by an onboard camera, and intercepting a section of the video record as a second target video record; Step S202: Obtain n frames from the second target video record, mark the unit area with picture errors in each frame, collect the unit areas with picture errors in the kth frame to form an error area, the error area is composed of adjacent unit areas, obtain the image of the error area, and record it as the error image im of the kth error area k ; Step S203: Gathering error images of each frame, arranging the errors according to the order of the pictures in the second target video record to obtain an error image sequence, calculating the image quality evaluation index of each error image, and arranging the error images according to the order to obtain an error value sequence; Step S204: Perform function fitting on the image quality evaluation index sequence to obtain a function of the image quality evaluation index changing with time, record the function as the error change function, obtain the flight posture and flight speed of the drone when shooting the second target video record and record them in the second state set R2, form an error change group with the error change function and the second state set R2, collect the corresponding relationship between several groups of error change functions and the second state set R2, collect the error change groups, and obtain the error change database.

4. The target feature random error analysis method based on cloud computing according to claim 3 is characterized by: The calculation method of image quality evaluation index includes: 2-1: Convert the image into a grayscale image, set u grayscale level judgment levels to quantify the grayscale image, and count the occurrence probability of each grayscale level in the grayscale image; 2-2: Get the probability p of the e-th gray level e , calculate the information entropy H of the grayscale image, ； 2-3: Use information entropy as an image quality evaluation indicator for the grayscale image.

5. The target feature random error analysis method based on cloud computing according to claim 3 is characterized by: Step S300 includes: Step S301: from the real-time video captured by the onboard camera of the drone, a first image and a second image are obtained in the order of video capture, and the contrasts of the first image and the second image are calculated, respectively, and are recorded as CR1 and CR2. When CR1>CR2, a contrast threshold M is calculated, where M=CR1-CR2. Step S302: Obtain the image slice corresponding to the jth unit area in the first image, denoted as psc 1j , the image slice corresponding to the jth unit area in the second image is denoted as psc 2j , get psc 1j The contrast is denoted as r 1j ,psc 2j The contrast is denoted as r 2j ; Step S303: When r 1j -r 2j >M, the j-th unit area in the second image is marked as the first target unit area.

6. The target feature random error analysis method based on cloud computing according to claim 5 is characterized by: Step S400 includes: Step S401: obtaining the timestamps of the first image and the second image captured by the drone's onboard camera, recording the time period from capturing the first image to capturing the second image as the target time period, obtaining the flight attitude and flight speed of the drone in the target time period, and recording them in the target state set RT; Step S402: acquiring a first state set identical to the target state set RT in a picture transfer database, and acquiring a vector coordinate β of an indicator vector associated with the first state set; Step S403: Obtain the coordinates ref of the first target unit area t1 , calculate the coordinates of the second target unit area ref t2 , ref t2 = ref t1 -β, the second target unit area is in the first image; Step S404: obtaining an image slice of the first target unit area, denoted as psc1, and an image slice of the second target unit area, denoted as psc2, and obtaining image quality evaluation indicators of psc1 and psc2, denoted as rq1 and rq2 respectively.

7. The target feature random error analysis method based on cloud computing according to claim 6 is characterized by: Step S500 includes: Step S501: acquiring a second state set identical to the target state set RT in an error change database, and acquiring an error change function associated with the second state set; Step S502: Marking a data point whose value is equal to the image quality evaluation index rq2 in the error change function, recording the data point as a first data point, and obtaining a data point of a time length of a target time period in the error change function as a second data point; Step S503: obtaining an image quality evaluation index corresponding to the second data point, and using the image quality evaluation index as an error reference value; Step S504: setting an error threshold γ, when |Q-rq1|>γ, marking the image slice psc1 and sending it to the relevant management personnel.

8. A target feature random error analysis system based on cloud computing, used to execute a target feature random error analysis method based on cloud computing according to any one of claims 1 to 7, characterized in that: The system includes: The invention relates to a picture movement management module, an error management module, a contrast comparison module, an image slice management module and an image judgment module, wherein the picture movement management module is used to manage the relative motion relationship between objects and viewing angles in the pictures taken by the onboard camera under different flight states, the error management module is used to manage the picture features of non-random errors, the contrast comparison module is used to compare the changes in contrast in the picture frames and obtain the first target unit area, the image slice management module is used to obtain the aircraft status record of the UAV and manage the image slices, and the image judgment module is used to judge whether there are non-random errors in the image slices.

9. The target feature random error analysis system based on cloud computing according to claim 8, characterized in that: The picture movement management module includes: a picture management unit, an indication vector management unit and a picture transfer database management unit, wherein the picture management unit is used to manage the unit management area in the picture taken by the airborne camera, the indication vector management unit is used to mark the relative motion relationship of the object taken in the state of the drone relative to the picture taken, and obtain the indication vector, and the picture transfer database management unit is used to manage the picture transfer database; The error management module includes: an error image management unit, an image quality evaluation unit, a function fitting unit and an error change database management unit, wherein the error image management unit is used to manage the pictures with non-random errors in the historical shooting records, the image quality evaluation unit is used to calculate the image quality evaluation index in the image, the function fitting unit is used to perform function fitting on the image quality evaluation index of the error image to obtain a function of the image quality evaluation index changing with time, and the error change database management unit is used to manage the error change database.

10. The target feature random error analysis system based on cloud computing according to claim 8, characterized in that: The contrast comparison module includes: a global contrast comparison unit and a local contrast comparison unit, wherein the global contrast comparison unit is used to compare the contrast of the complete image and obtain a contrast threshold, and the local contrast comparison unit is used to compare the contrast in a unit area in the image and obtain a first target unit area; The image slice management module includes: a state management unit and an image area tracing unit, wherein the state management unit is used to obtain the real-time flight state record of the UAV, and the image area tracing unit is used to trace the image of the first target unit area to obtain the image of the second target unit area; The image judgment module includes: a reference value management unit, an error judgment unit and an information feedback unit, wherein the reference value management unit is used to obtain an error reference value, the error judgment unit is used to judge the difference between the image quality evaluation index and the error reference value, and the information feedback unit is used to mark the image slices that meet the judgment conditions and send them to relevant managers.

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