Image-based automobile loss assessment method and system

Abstract

The invention relates to an image-based automobile loss assessment method and system. The method comprises the following steps: acquiring a comparison reference picture group and a current return picture group of an automobile; generating a comparison model by using the comparison reference picture group and generating a current model by using the current return picture group; the comparison model and the current model are compared, and a distinguishing feature area existing on the current model is found; analyzing contents included in the distinguishing feature region, wherein the contents include damaged contents and interference contents; and when the damaged content exists in the content included in the distinguishing feature region, determining the type of the damaged content, and determining a loss type and / or a loss price according to the type of the damaged content. According to the image-based automobile loss assessment method and system disclosed by the invention, the damaged content and the interference content can be found by referring to the image and the current image, the damaged content is evaluated at the same time, the mode has the same evaluation standard, and the damage condition can be comprehensively evaluated.

Description

Technical Field

[0001] The present application relates to the technical field of image data processing, and in particular to an image-based automobile damage assessment method and system. Background Art

[0002] Automobile damage assessment refers to the evaluation of the damage to the vehicle, which is mainly divided into two aspects: traffic accident damage assessment and stage damage assessment. Traffic accident damage assessment is mainly participated by insurance companies, insurance agents and 4S stores, and has a relatively complete process. Stage damage assessment is mainly used in the car rental industry, because the damage needs to be evaluated every time the vehicle is returned.

[0003] The original evaluation method was mostly conducted by store staff, which had problems such as inconsistent evaluation standards, insufficient evaluation capabilities, and omissions. Currently, there is a problem of using image comparison for judgment, but for large areas of car paint, there are still certain deficiencies in identifying scars and before-and-after comparison. An important reason is that the generation standards of the before-and-after images are inconsistent, and there are a certain number of judgment errors and omissions. Summary of the invention The present application provides an image-based automobile damage assessment method and system, which can discover damaged content and interference content by referring to pictures and current pictures, and evaluate the damaged content at the same time. This method has the same evaluation standard and can comprehensively evaluate the damage situation.

[0004] The above-mentioned purpose of the present application is achieved through the following technical solutions: In a first aspect, the present application provides an image-based automobile damage assessment method, comprising: Obtain the vehicle's comparison reference picture group and current returned picture group; Using the comparison reference picture set to generate a comparison model and using the current return picture set to generate a current model; Compare the comparison model with the current model to find the distinguishing feature areas existing on the current model; Analyze and distinguish the contents included in the characteristic area, including damaged contents and interference contents; When there is damaged content in the content included in the distinguishing feature area, determining the type of the damaged content, the type of the damaged content includes newly added damaged content and original damaged and spread content; The type of loss and / or loss price is determined based on the type of damaged content.

[0005] In a possible implementation manner of the first aspect, comparing the comparison model with the current model and finding a distinguishing feature region on the current model includes: Identify the object type in the comparison model, recorded as the first object type; Identify the object type in the current model, recorded as the second object type; matching a first object type and a second object type, the first object type and the second object type belonging to the same automobile component; Creating a first guiding analysis line on the comparison reference picture group by means of the first object type that is successfully matched; Creating a second guide analysis line on the currently returned picture group by means of the successfully matched second object type, wherein the positions of two end points of the first guide analysis line and two end points of the second guide analysis line on the comparison reference picture group are the same as those on the currently returned picture group; synchronously moving the first guide analysis line and the second guide analysis line; During the movement, the changing trend of the first guide analysis line and the changing trend of the second guide analysis line are recorded; The distinguishing characteristic regions are determined using the changing trends on the first guided analysis line and the changing trends on the second guided analysis line.

[0006] In a possible implementation manner of the first aspect, recording a change trend on the first guide analysis line and a change trend on the second guide analysis line includes: Using the pixel points on the first guiding analysis line to establish an analysis curve and determine a first sudden change area on the analysis curve; Using the pixel points on the second guiding analysis line to establish an analysis curve and determine a second sudden change area on the analysis curve; Wherein, the pixel points on the first guide analysis line and the pixel points on the second guide analysis line are both subjected to grayscale processing or monochrome processing.

[0007] In a possible implementation manner of the first aspect, when determining the distinguishing feature area using the change trend on the first guide analysis line and the change trend on the second guide analysis line, the method further includes: Using the interference content to screen the first sudden change region and the second sudden change region, and retaining the first sudden change region and the second sudden change region that are not related to the interference content; Determine the correspondence between the first sudden change region and the second sudden change region to be retained according to the distance parameter or the sudden change parameter; The second sudden change region with no corresponding relationship and with incomplete corresponding relationship is used as the distinguishing characteristic region.

[0008] In a possible implementation manner of the first aspect, the content of analyzing the distinguishing characteristic area includes: Determine the distinctive color areas on the current model; Creating difference guide lines based on the difference color areas, where the number of difference guide lines is at least one; Use the difference guide line to determine the content included in the distinguishing feature area, including damaged content and interference content.

[0009] In a possible implementation manner of the first aspect, determining the type of damaged content includes: Correct the current model according to the comparison model so that the current model coincides with the comparison model; comparing the damaged content on the corrected current model and the comparison model; Determine the type of damaged content. When the damaged content only appears on the corrected current model, determine the type of content as newly added damaged content. When the damaged content on the comparison model is located inside the damaged content of the corrected current model, determine the type of content as original damaged spread content.

[0010] In a possible implementation manner of the first aspect, correcting the current model according to the comparison model includes: Identify the object type in the comparison model, recorded as the first object type; Identify the object type in the current model, recorded as the second object type; matching a first object type and a second object type, the first object type and the second object type belonging to the same automobile component; driving the second object type to coincide with the matching first object type; When the first object type and the matching second object type cannot completely overlap, the area of ​​the enclosed region between the edge of the first object type and the edge of the second object type is minimized, and the area of ​​the enclosed region includes positive area and negative area.

[0011] In a second aspect, the present application provides an image-based automobile damage assessment device, comprising: A data acquisition unit, used to acquire a comparison reference picture group and a current return picture group of the vehicle; A model generation unit, used to generate a comparison model using a comparison reference picture group and to generate a current model using a current return picture group; A model comparison unit, used for comparing the comparison model with the current model, and finding the distinguishing feature areas existing on the current model; A content analysis unit, used for analyzing the content included in the distinguishing characteristic area, the content including damaged content and interference content; A type determination unit, configured to determine the type of damaged content when damaged content exists in the content included in the distinguishing feature area, wherein the type of damaged content includes newly added damaged content and original damaged and spread content; The damage assessment unit is used to determine the loss type and / or loss price according to the type of damaged content.

[0012] In a third aspect, the present application provides an image-based automobile damage assessment system, the system comprising: one or more memories for storing instructions; and One or more processors, used to call and run the instructions from the memory to execute the method as described in the first aspect and any possible implementation of the first aspect.

[0013] In a fourth aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium comprising: Program, when the program is executed by a processor, the method described in the first aspect and any possible implementation of the first aspect is executed.

[0014] In a fifth aspect, the present application provides a computer program product, comprising program instructions. When the program instructions are executed by a computing device, the method described in the first aspect and any possible implementation of the first aspect is executed.

[0015] In a sixth aspect, the present application provides a chip system, which includes a processor for implementing the functions involved in the above aspects, for example, generating, receiving, sending, or processing the data and / or information involved in the above methods.

[0016] The chip system may be composed of chips, or may include chips and other discrete devices.

[0017] In a possible design, the chip system also includes a memory, which is used to store necessary program instructions and data. The processor and the memory can be decoupled and respectively set on different devices, connected by wired or wireless means, or the processor and the memory can also be coupled on the same device. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic flowchart of the steps of an image-based automobile damage assessment method provided by the present application.

[0019] Figure 2 It is a schematic diagram of a shooting position provided by this application.

[0020] Figure 3 It is a schematic diagram of a guide analysis line on a picture provided by this application.

[0021] Figure 4 This is a schematic diagram of the principle of obtaining a sudden change area provided by the present application.

[0022] Figure 5 This is a schematic diagram of the principle of establishing a difference guide line provided by the present application.

[0023] Figure 6 It is a schematic diagram of positive area and negative area provided in this application. DETAILED DESCRIPTION

[0024] The technical solution in this application is further described in detail below in conjunction with the accompanying drawings.

[0025] This application discloses a method for assessing automobile damage based on images. Figure 1 In some examples, the image-based automobile damage assessment method disclosed in this application includes the following steps: S101, obtaining a comparison reference picture group and a current return picture group of a vehicle; S102, using the comparison reference picture group to generate a comparison model and using the current return picture group to generate a current model; S103, comparing the comparison model and the current model to find the distinguishing feature area existing in the current model; S104, analyzing and distinguishing the content included in the characteristic area, the content including damaged content and interference content; S105, when there is damaged content in the content included in the distinguishing characteristic area, determining the type of the damaged content, where the type of the damaged content includes newly added damaged content and original damaged and spread content; S106, determining the loss type and / or loss price according to the type of damaged content.

[0026] The contents in step S101 to step S106 are to discover the damage contents by comparing the comparison reference picture group of the vehicle with the current return picture group. The comparison reference picture group of the vehicle refers to the delivery photos of the vehicle, the photos of the last return or the photos after repair, and the current return picture group of the vehicle refers to the photos obtained after taking the vehicle now.

[0027] The specific method of using the comparison reference picture group to generate the comparison model is to determine the relative position of the comparison reference picture in the comparison reference picture group and the vehicle. Here, the shooting position of each picture needs to be determined. The general shooting positions include the front, rear, left, right and special fixed positions (such as the wheel hub), such as Figure 2 shown.

[0028] The comparison model includes the image and the location where the image was taken, and similarly the current model also includes the image and the location where the image was taken. The main reason for requiring the shooting location is that different shooting locations will cause the shape of the captured content to change, which may easily lead to errors in the subsequent judgment process.

[0029] Then, the distinguishing feature areas existing in the current model are found and classified. There are two classification results: the first is damaged content and the second is interference content. For damaged content, further judgment is required, and the specific method is to determine the type of damaged content. The types of damaged content include newly added damaged content and original damaged content.

[0030] Newly added damage content refers to newly appeared scars, and the spread of original damage content refers to the expansion of the original old scars, such as the increase in area.

[0031] Finally, the loss type and / or loss price is determined based on the type of damaged content. The loss type includes all kinds of damage currently involved in the appearance. As for the determination of the loss price, the specific processing method is to first determine the location, type and area of ​​the damage, and then determine the price based on the query method. Of course, the price here is only for reference and is not the final actual price.

[0032] At the same time, in order to make the price have a certain reference value, this application needs to give different prices for different treatment methods at the same time. Taking car door scratches as an example, it is necessary to give the price of partial painting and the price of overall painting.

[0033] In summary, for the loss price, its essential implementation method is to consult the data query library through conditions and give the result. The accuracy of the loss price lies in the accuracy and matching of the data in the data query library.

[0034] In some examples, specific ways of comparing the comparison model with the current model and finding the distinguishing feature areas existing on the current model are: S201, identifying an object type in a comparison model, recorded as a first object type; S202, identifying the object type in the current model, recorded as the second object type; S203, matching a first object type and a second object type, where the first object type and the second object type belong to the same automobile component; S204, creating a first guiding analysis line on the comparison reference picture group by using the first object type that is successfully matched; S205, creating a second guide analysis line on the currently returned picture group by using the successfully matched second object type, where two endpoints of the first guide analysis line and two endpoints of the second guide analysis line are located at the same positions on the comparison reference picture group and on the currently returned picture group; S206, synchronously moving the first guide analysis line and the second guide analysis line; S207, recording a change trend on the first guide analysis line and a change trend on the second guide analysis line during the movement; S208: Determine a distinguishing feature area using the changing trend on the first guide analysis line and the changing trend on the second guide analysis line.

[0035] In step S201 to step S203, it is necessary to determine whether the first object type and the second object type belong to the same automobile component and then compare them, that is, it is necessary to compare the previous state and the next state of the same automobile component to find the difference.

[0036] See also Figure 3 The specific process of comparison is to compare the reference picture group ( Figure 3 The first guide analysis line is created on the left rectangle in the middle and on the current returned image group ( Figure 3 A second guide analysis line is created on the rectangle on the right side of the image (a), and the positions of the two endpoints of the first guide analysis line and the two endpoints of the second guide analysis line on the comparison reference picture group and on the current returned picture group are the same.

[0037] After the first guide analysis line and the second guide analysis line are created, the first guide analysis line and the second guide analysis line are synchronously moved and the change trend on the first guide analysis line and the change trend on the second guide analysis line are recorded.

[0038] The moving direction reference of the first guide analysis line and the second guide analysis line Figure 3 Arrow in the.

[0039] At this time, if the change trend on the first guide analysis line is the same as the change trend on the second guide analysis line, it means that the previous state and the next state at the position are consistent, otherwise it means that the previous state and the next state at the position are inconsistent.

[0040] At this time, the changing trend on the first guide analysis line and the changing trend on the second guide analysis line can be used to determine the distinguishing feature area.

[0041] This method is a comprehensive comparison method, because the point can be found through the change trend, and then the distinguishing feature area can be found through the point. At this time, some sun patterns or slight scratches can be actively ignored by limiting the change trend.

[0042] The specific method of recording the changing trend of the first guide analysis line and the changing trend of the second guide analysis line is as follows: Using the pixel points on the first guiding analysis line to establish an analysis curve and determine a first sudden change area on the analysis curve; Using the pixel points on the second guiding analysis line to establish an analysis curve and determine a second sudden change area on the analysis curve; Wherein, the pixel points on the first guide analysis line and the pixel points on the second guide analysis line are both subjected to grayscale processing or monochrome processing.

[0043] In the above content, the method of determining the sudden change area is to use the pixel points on the guide analysis line to establish an analysis curve, and then determine the first sudden change area and the second sudden change area. If the first sudden change area and the second sudden change area are matched, it means that the previous and subsequent conditions at the position are consistent, and the damage at the position is an existing old injury. Otherwise, it means that the previous and subsequent conditions at the position are inconsistent, and the damage at the position is a new injury.

[0044] The way to establish the analysis curve is to use the sequential position of the pixel points as the horizontal coordinate and the value of the pixel points as the vertical coordinate, then obtain a set of discrete points in the coordinate system, and then connect these discrete points in sequence.

[0045] See also Figure 4 The way to determine the sudden change area (the first sudden change area, the second sudden change area) is to process the analysis curve using wavelet decomposition. At this time, a set of curves will be obtained. These curves have clear starting positions and end positions. When the length of the curve is not equal to the length of the analysis curve, the area corresponding to these curves is regarded as a potential sudden change area.

[0046] Then filter by length and amplitude. Here, a threshold is assigned to the length and amplitude respectively. When the length of the curve is less than the corresponding threshold or the amplitude is less than the corresponding threshold, the curve will be discarded. Combined with the above content, it can be known that this is mainly to shield sun streaks and some minor scratches.

[0047] In some examples, when determining the distinguishing feature area using the change trend on the first guide analysis line and the change trend on the second guide analysis line, the following content is also added: Using the interference content to screen the first sudden change region and the second sudden change region, and retaining the first sudden change region and the second sudden change region that are not related to the interference content; Determine the corresponding relationship between the first sudden change area and the second sudden change area to be retained according to the distance parameter or the sudden change parameter; The second sudden change region with no corresponding relationship and with incomplete corresponding relationship is used as the distinguishing characteristic region.

[0048] The reason for using the interference content to screen the first sudden change region and the second sudden change region is that the interference content will interfere with the determination of the sudden change region. Therefore, it is necessary to use the interference content to screen the first sudden change region and the second sudden change region. During the screening, the first sudden change region and the second sudden change region that are not related to the interference content are retained.

[0049] The interference content is mainly the dirt and stains attached to the car body, because the interference content will directly lead to the appearance of sudden change areas, but these sudden change areas are not damage and need to be removed.

[0050] In the above method, discrete points are obtained by using the analysis curve, and then the discrete points appearing on the area where the analysis curve passes are connected to obtain closed areas, and these areas are the distinguishing feature areas.

[0051] In some examples, the specific manner of analyzing the content included in the distinguishing feature area is as follows: S301, determining a distinguishing color area on the current model; S302, creating difference guide lines based on the distinguishing color areas, where the number of difference guide lines is at least one; S303, using the difference guide line to determine the content included in the distinguishing feature area, the content includes damaged content and interference content.

[0052] In step S301 to step S303, first find the distinguishing color area on the current model. The distinguishing color area refers to the area with obvious color difference on the current model. After finding the distinguishing color area, create a difference guide line in the distinguishing color area and analyze the color change trend on the difference guide line. Finally, determine the content of the distinguishing feature area based on the color change trend.

[0053] Here, the sudden change area recorded in the above content is further described. The cause of the sudden change area may be damage or stains on the car body. When assessing the damage, the sudden change area caused by the stains on the car body needs to be removed.

[0054] Here, the distinguishing color areas can be divided into three categories, namely, dent category, scratch category and stain category. The color difference between the distinguishing color areas of the dent category and the surrounding areas is not large, but the color difference between the distinguishing color areas of the scratch category and the stain category and the surrounding areas is large.

[0055] Creating difference guide lines based on the difference color area refers to randomly establishing multiple straight lines on the difference color area, and then calculating the difference between adjacent pixel points on the straight line. If the difference is less than a set threshold, it means that the difference color area is classified as a dent. If the difference is greater than a set threshold, it means that the difference color area is classified as a scratch or stain.

[0056] The scratch and stain categories are further differentiated as follows: See also Figure 5First, a point is randomly selected on the color difference area, and then multiple difference guide lines passing through the point are set at the point, and then the change point on the difference guide line is determined. The change point refers to the maximum difference point that appears in the direction close to the randomly selected point. The maximum difference point is the edge of the color difference area.

[0057] The number of maximum difference points is at least two, and then the distance between the two maximum difference points is calculated. For scratches, the distance span between the two maximum difference points will be significantly greater than the distance span between the two maximum difference points belonging to stains.

[0058] At this time, there is still a potential problem that some small stains are identified as scratches. The solution to this problem is to classify the different color areas according to color and sort them from large to small according to the number of classifications, and then delete the first or first few different color areas after sorting.

[0059] After the deletion process is performed, the different color areas of stains can be removed as much as possible.

[0060] In some examples, the type of corrupted content is determined by: S401, correcting the current model according to the comparison model so that the current model coincides with the comparison model; S402, comparing the damaged contents on the corrected current model and the comparison model; S403, determine the type of damaged content. When the damaged content only appears on the corrected current model, determine the type of content as newly added damaged content. When the damaged content on the comparison model is located inside the damaged content of the corrected current model, determine the type of content as original damaged spread content.

[0061] In the above comparison method, the type of damaged content is determined by comparing the overlapped content. The specific rules are as follows: When the damaged content only appears on the corrected current model, the type of the content is determined to be newly added damaged content; When the damaged content on the comparison model is located inside the damaged content of the corrected current model, the type of the content is determined to be the original damaged diffusion content.

[0062] The specific method of correcting the current model according to the comparison model mentioned above is as follows: Identify the object type in the comparison model, recorded as the first object type; Identify the object type in the current model, recorded as the second object type; matching a first object type and a second object type, the first object type and the second object type belonging to the same automobile component; driving the second object type to coincide with the matching first object type; When the first object type and the matching second object type cannot completely overlap, the area of ​​the enclosed region between the edge of the first object type and the edge of the second object type is minimized, and the area of ​​the enclosed region includes positive area and negative area, such as Figure 6 shown.

[0063] Specifically, type matching is first performed, which requires that the first object type and the second object type belong to the same automobile component, and then the second object type is driven to overlap with the matching first object type. The specific driving method is to make the second object type overlap with the matching first object type by moving.

[0064] When the first object type and the matching second object type cannot completely overlap, they are still processed by moving, but the area of ​​the enclosed area between the edge of the first object type and the edge of the second object type needs to be minimized. The enclosed area area has two types: positive area and negative area. Here, the edge of the first object type is used as a reference. The area of ​​the enclosed area inside the edge of the first object type is the positive area, and the area of ​​the enclosed area outside the edge of the first object type is the negative area.

[0065] The purpose of this approach is to make the first object type and the matching second object type overlap as much as possible.

[0066] The present application also provides an image-based automobile damage assessment device, comprising: A data acquisition unit, used to acquire a comparison reference picture group and a current return picture group of the vehicle; A model generation unit, used to generate a comparison model using a comparison reference picture group and to generate a current model using a current return picture group; A model comparison unit, used for comparing the comparison model with the current model, and finding the distinguishing feature areas existing on the current model; A content analysis unit, used for analyzing the content included in the distinguishing characteristic area, the content including damaged content and interference content; A type determination unit, configured to determine the type of damaged content when damaged content exists in the content included in the distinguishing feature area, wherein the type of damaged content includes newly added damaged content and original damaged and spread content; The damage assessment unit is used to determine the loss type and / or loss price according to the type of damaged content.

[0067] In a possible implementation manner of the first aspect, comparing the comparison model with the current model and finding a distinguishing feature region on the current model includes: Identify the object type in the comparison model, recorded as the first object type; Identify the object type in the current model, recorded as the second object type; matching a first object type and a second object type, the first object type and the second object type belonging to the same automobile component; Creating a first guiding analysis line on the comparison reference picture group by means of the first object type that is successfully matched; Creating a second guide analysis line on the currently returned picture group by means of the successfully matched second object type, wherein the positions of two end points of the first guide analysis line and two end points of the second guide analysis line on the comparison reference picture group are the same as those on the currently returned picture group; synchronously moving the first guide analysis line and the second guide analysis line; During the movement, the changing trend of the first guide analysis line and the changing trend of the second guide analysis line are recorded; The distinguishing characteristic regions are determined using the changing trends on the first guided analysis line and the changing trends on the second guided analysis line.

[0068] In a possible implementation manner of the first aspect, recording a change trend on the first guide analysis line and a change trend on the second guide analysis line includes: Using the pixel points on the first guiding analysis line to establish an analysis curve and determine a first sudden change area on the analysis curve; Using the pixel points on the second guiding analysis line to establish an analysis curve and determine a second sudden change area on the analysis curve; Wherein, the pixel points on the first guide analysis line and the pixel points on the second guide analysis line are both subjected to grayscale processing or monochrome processing.

[0069] In a possible implementation manner of the first aspect, when determining the distinguishing feature area using the change trend on the first guide analysis line and the change trend on the second guide analysis line, the method further includes: Using the interference content to screen the first sudden change region and the second sudden change region, and retaining the first sudden change region and the second sudden change region that are not related to the interference content; Determine the corresponding relationship between the first sudden change area and the second sudden change area to be retained according to the distance parameter or the sudden change parameter; The second sudden change region with no corresponding relationship and with incomplete corresponding relationship is used as the distinguishing characteristic region.

[0070] In a possible implementation manner of the first aspect, the content of analyzing the distinguishing characteristic area includes: Determine the distinctive color areas on the current model; Creating difference guide lines based on the difference color areas, where the number of difference guide lines is at least one; Use the difference guide line to determine the content included in the distinguishing feature area, including damaged content and interference content.

[0071] In a possible implementation manner of the first aspect, determining the type of damaged content includes: Correct the current model according to the comparison model so that the current model coincides with the comparison model; comparing the damaged content on the corrected current model and the comparison model; Determine the type of damaged content. When the damaged content only appears on the corrected current model, determine the type of content as newly added damaged content. When the damaged content on the comparison model is located inside the damaged content of the corrected current model, determine the type of content as original damaged spread content.

[0072] In a possible implementation manner of the first aspect, correcting the current model according to the comparison model includes: Identify the object type in the comparison model, recorded as the first object type; Identify the object type in the current model, recorded as the second object type; matching a first object type and a second object type, the first object type and the second object type belonging to the same automobile component; driving the second object type to coincide with the matching first object type; When the first object type and the matching second object type cannot completely overlap, the area of ​​the enclosed region between the edge of the first object type and the edge of the second object type is minimized, and the area of ​​the enclosed region includes positive area and negative area.

[0073] In one example, the unit in any of the above devices can be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or, one or more digital signal processors (DSPs), or, one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0074] For another example, when the units in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these units can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0075] Various objects such as various messages / information / equipment / network elements / systems / devices / actions / operations / processes / concepts that may appear in this application are named. It can be understood that these specific names do not constitute a limitation on the relevant objects. The names assigned may change with factors such as scenarios, contexts or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from the functions and technical effects embodied / executed in the technical scheme.

[0076] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0077] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0078] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0080] It should also be understood that in various embodiments of the present application, the first, second, etc. are only used to indicate that multiple objects are different. For example, the first time window and the second time window are only used to indicate different time windows. They should not have any impact on the time window itself, and the first, second, etc. mentioned above should not impose any limitations on the embodiments of the present application.

[0081] It should also be understood that in the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0082] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a computer-readable storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned computer-readable storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0083] The present application also provides an image-based automobile damage assessment system, the system comprising: one or more memories for storing instructions; and One or more processors are used to call and run the instructions from the memory to execute the method as described above.

[0084] The present application also provides a computer program product, which includes instructions. When the instructions are executed, the terminal device and the network device perform operations of the terminal device and the network device corresponding to the above method.

[0085] The present application also provides a chip system, which includes a processor for implementing the functions involved in the above content, such as generating, receiving, sending, or processing the data and / or information involved in the above method.

[0086] The chip system may be composed of chips, or may include chips and other discrete devices.

[0087] The processor mentioned in any of the above places can be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for executing programs for controlling the above-mentioned feedback information transmission method.

[0088] In a possible design, the chip system also includes a memory, which is used to store necessary program instructions and data. The processor and the memory can be decoupled and respectively set on different devices, connected by wire or wireless means to support the chip system to implement various functions in the above embodiments. Alternatively, the processor and the memory can also be coupled on the same device.

[0089] Optionally, the computer instructions are stored in a memory.

[0090] Optionally, the memory is a storage unit within the chip, such as a register, a cache, etc. The memory can also be a storage unit within the terminal located outside the chip, such as a ROM or other types of static storage devices that can store static information and instructions, RAM, etc.

[0091] It can be understood that the memory in the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0092] The non-volatile memory may be a ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory.

[0093] The volatile memory may be a RAM, which is used as an external cache. There are many different types of RAM, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct memory bus RAM.

[0094] The embodiments of this specific implementation method are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Therefore, all equivalent changes made based on the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. An image-based automobile damage assessment method, characterized in that: include: Obtain the vehicle's comparison reference picture group and current returned picture group; Using the comparison reference picture set to generate a comparison model and using the current return picture set to generate a current model; Compare the comparison model with the current model to find the distinguishing feature areas existing on the current model; Analyze and distinguish the contents included in the characteristic area, including damaged contents and interference contents; When there is damaged content in the content included in the distinguishing feature area, determining the type of the damaged content, the type of the damaged content includes newly added damaged content and original damaged and spread content; The type of loss and / or loss price is determined based on the type of damaged content.

2. The image-based automobile damage assessment method according to claim 1, characterized in that: Areas where the comparison model is compared to the current model and distinguishing features found on the current model include: Identify the object type in the comparison model, recorded as the first object type; Identify the object type in the current model, recorded as the second object type; matching a first object type and a second object type, the first object type and the second object type belonging to the same automobile component; Creating a first guiding analysis line on the comparison reference picture group by means of the first object type that is successfully matched; Creating a second guide analysis line on the currently returned picture group by means of the successfully matched second object type, wherein the positions of two end points of the first guide analysis line and two end points of the second guide analysis line on the comparison reference picture group are the same as those on the currently returned picture group; synchronously moving the first guide analysis line and the second guide analysis line; During the movement, the changing trend of the first guide analysis line and the changing trend of the second guide analysis line are recorded; The distinguishing characteristic regions are determined using the changing trends on the first guided analysis line and the changing trends on the second guided analysis line.

3. The image-based automobile damage assessment method according to claim 2, characterized in that: Recording the changing trend of the first guide analysis line and the changing trend of the second guide analysis line includes: Using the pixel points on the first guiding analysis line to establish an analysis curve and determine a first sudden change area on the analysis curve; Using the pixel points on the second guiding analysis line to establish an analysis curve and determine a second sudden change area on the analysis curve; Wherein, the pixel points on the first guide analysis line and the pixel points on the second guide analysis line are both subjected to grayscale processing or monochrome processing.

4. The image-based automobile damage assessment method according to claim 3, characterized in that: When determining the distinguishing characteristic area using the change trend on the first guide analysis line and the change trend on the second guide analysis line, the method further includes: Using the interference content to screen the first sudden change region and the second sudden change region, and retaining the first sudden change region and the second sudden change region that are not related to the interference content; Determine the corresponding relationship between the first sudden change area and the second sudden change area to be retained according to the distance parameter or the sudden change parameter; The second sudden change region with no corresponding relationship and with incomplete corresponding relationship is used as the distinguishing characteristic region.

5. The image-based automobile damage assessment method according to any one of claims 1 to 4, characterized in that: The analysis of the distinguishing characteristic areas includes: Determine the distinctive color areas on the current model; Creating difference guide lines based on the difference color areas, where the number of difference guide lines is at least one; Use the difference guide line to determine the content included in the distinguishing feature area, including damaged content and interference content.

6. The image-based automobile damage assessment method according to claim 1, characterized in that: Identify the types of damaged content including: Correct the current model according to the comparison model so that the current model coincides with the comparison model; comparing the damaged content on the corrected current model and the comparison model; Determine the type of damaged content. When the damaged content only appears on the corrected current model, determine the type of content as newly added damaged content. When the damaged content on the comparison model is located inside the damaged content of the corrected current model, determine the type of content as original damaged spread content.

7. The image-based automobile damage assessment method according to claim 6, characterized in that: Calibrating the current model according to the comparison model includes: Identify the object type in the comparison model, recorded as the first object type; Identify the object type in the current model, recorded as the second object type; matching a first object type and a second object type, the first object type and the second object type belonging to the same automobile component; driving the second object type to coincide with the matching first object type; When the first object type and the matching second object type cannot completely overlap, the area of ​​the enclosed region between the edge of the first object type and the edge of the second object type is minimized, and the area of ​​the enclosed region includes positive area and negative area.

8. An image-based automobile damage assessment device, characterized in that: include: A data acquisition unit, used to acquire a comparison reference picture group and a current return picture group of the vehicle; A model generation unit, used to generate a comparison model using a comparison reference picture group and to generate a current model using a current return picture group; A model comparison unit, used for comparing the comparison model with the current model, and finding the distinguishing feature areas existing on the current model; A content analysis unit, used for analyzing the content included in the distinguishing characteristic area, the content including damaged content and interference content; A type determination unit, configured to determine the type of damaged content when damaged content exists in the content included in the distinguishing feature area, wherein the type of damaged content includes newly added damaged content and original damaged and spread content; The damage assessment unit is used to determine the loss type and / or loss price according to the type of damaged content.

9. An image-based automobile damage assessment system, characterized in that: The system comprises: one or more memories for storing instructions; and One or more processors, configured to call and execute the instructions from the memory to perform the method according to any one of claims 1 to 7.

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

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