CN110017998B - Vehicle detection method, device and equipment - Google Patents
Vehicle detection method, device and equipment Download PDFInfo
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- CN110017998B CN110017998B CN201910232654.XA CN201910232654A CN110017998B CN 110017998 B CN110017998 B CN 110017998B CN 201910232654 A CN201910232654 A CN 201910232654A CN 110017998 B CN110017998 B CN 110017998B
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Abstract
The invention discloses a vehicle detection method, a vehicle detection device and vehicle detection equipment. The method comprises the following steps: acquiring a first image of a vehicle to be detected; comparing the first image with a second image of a standard vehicle to determine a difference area; and identifying the difference type of the difference area to determine the detection result of the vehicle to be detected. Through the technical scheme, when the value of the vehicle to be estimated is evaluated, the detection of the conditions of all the parts in the vehicle can be realized without disassembling the vehicle, the detection efficiency can be effectively improved, and the accuracy of the detection result is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a vehicle detection method, a vehicle detection device and vehicle detection equipment.
Background
In the prior art, when detecting the safety condition of a vehicle, the detection is generally divided into appearance detection and interior component detection. The appearance detection is usually to the integrality of each part such as car lacquer mar, car lacquer thickness and automobile body panel beating detect, and the appearance detection is usually fairly simple. When the parts in the vehicle are detected, the parts are usually disassembled, so that the disassembly detection of the vehicle body is time-consuming and labor-consuming; and some owners do not want the vehicle to be disassembled, so that detailed and comprehensive detection cannot be carried out. The detection labor cost is high, and the detection time is long. Further, the inspector cannot visually check the presence of cracks in the component or minute variations in the component size.
Based on the above scheme, a scheme capable of comprehensively detecting the interior of the vehicle without disassembling the vehicle is needed.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a vehicle detection method, device and apparatus, which are used to implement a scheme capable of performing comprehensive detection on the interior of a vehicle without disassembling the vehicle.
In a first aspect, an embodiment of the present invention provides a vehicle detection method, including:
acquiring a first image of a vehicle to be detected;
comparing the first image with a second image of a standard vehicle to determine a difference area;
and identifying the difference type of the difference area to determine the detection result of the vehicle to be detected.
Further, after acquiring the scanned image of the vehicle to be detected, the method further comprises:
according to the material category and the thickness of a part in the vehicle to be detected, performing color rendering on the part; wherein, different material categories correspond to different color categories, and the same material categories with different thicknesses correspond to the same color categories with different shades.
Further, before determining the difference region, the method further includes:
identifying a target collation point in the first image;
and aligning a preset calibration point in the second image with the target calibration point.
Further, the identifying the difference type of the difference region includes:
and if the difference area lacks a part graph, determining that the vehicle to be detected lacks a corresponding part.
Further, if it is determined that the component corresponding to the difference region is not absent, the identifying the difference type of the difference region includes:
and if the color types of the different areas are different, determining that the material types of the parts corresponding to the vehicle to be detected are different.
Further, if the material types of the difference regions are the same, the identifying the difference types of the difference regions includes:
and if the different areas have the same color types with different depths, determining that the thicknesses of the corresponding parts of the vehicle to be detected are different.
Further, the identifying the difference type of the difference region includes:
and if the part graph of the difference area has the linear trace, determining that the part corresponding to the vehicle to be detected has the crack.
Further, still include: determining the fraction score, the material category difference score, the thickness difference score and the crack score.
Further, still include: and determining the value of the vehicle to be detected according to the missing part score, the material class difference score, the thickness difference score and the crack score which respectively correspond to each difference class.
In a second aspect, an embodiment of the present invention provides a vehicle detection apparatus, including:
the acquisition module is used for acquiring a first image of a vehicle to be detected;
the difference determining module is used for comparing the first image with a second image of a standard vehicle to determine a difference area;
and the detection module is used for identifying the difference type of the difference area so as to determine the detection result of the vehicle to be detected.
In a third aspect, an electronic device includes a processor, a memory to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement:
acquiring a first image of a vehicle to be detected;
comparing the first image with a second image of a standard vehicle to determine a difference area;
and identifying the difference type of the difference area to determine the detection result of the vehicle to be detected.
In the embodiment of the invention, the image scanning is carried out on the vehicle to be detected, the scanned image obtained by scanning is compared with the second image obtained in advance, and the difference points in the scanned image and the second image are searched. And further, according to a preset identification rule, identifying the difference types of the difference points, generating identification results corresponding to the difference points, and estimating the value of the vehicle to be detected according to the difference identification results. Based on the scheme, when the value of the vehicle to be estimated is evaluated, the detection of the conditions of all parts in the vehicle can be realized without disassembling the vehicle, the detection efficiency can be effectively improved, and the accuracy of the detection result is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of a vehicle detection method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a vehicle detection device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device corresponding to the vehicle detection method provided in the embodiment of fig. 1.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and "a" and "an" generally include at least two, but do not exclude at least one, unless the context clearly dictates otherwise.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
It is also noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a good or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such good or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a commodity or system that includes the element.
Fig. 1 is a schematic flowchart of a vehicle detection method according to an embodiment of the present invention, and as shown in fig. 1, an execution subject of the method may be a server or other devices. The method comprises the following steps:
101: a first image of a vehicle to be detected is acquired.
The first image of the vehicle to be detected here is understood to be a first image (scanned image) generated by scanning the vehicle to be detected with the X-ray device. For example, a vehicle to be detected can be parked right below the X-ray device, and the X-ray device performs a downward shooting to obtain a first image; and further preprocessing the first image to remove interference factors, such as filtering out water cups, tools and other articles which are not in the vehicle and are placed in the vehicle, so as to obtain the first image formed by the vehicle parts. In practical application, in order to provide better viewing effect for users, the first image can be rendered according to the X-ray value.
102: and comparing the first image with a second image of a standard vehicle to determine a difference area.
The second image is an image generated by X-ray scanning of a standard vehicle by an X-ray device. The second image may be an image directly obtained after X-ray scanning or an image subjected to color rendering. It should be noted that the standard vehicle is the same type as the vehicle to be detected. In practical application, the second images of vehicles of various models may be stored at the same time so as to meet the detection requirements of the vehicles of various models.
When the first image is compared with the second image, for better detection, a mode of comparing pixel points one by one can be adopted, if the X-ray value or the color type or the color depth corresponding to the pixel point is different, the pixel point is marked, and a difference area is formed based on the marked pixel points. It is to be understood that the difference region may be a region formed by at least one pixel, and the difference region may be in a linear shape.
103: and identifying the difference type of the difference area to determine the detection result of the vehicle to be detected.
As previously described, the zone of discrepancy in the vehicle to be detected can be determined through the steps previously described. In practical application, the problem of the part corresponding to the difference area needs to be subjected to detailed inspection, the problem of the part is determined, and the value of the part is evaluated according to the problem of the part.
In the identification process, a certain identification procedure is usually followed, for example, whether a part exists in the difference region is detected, and if the part exists, the material type, the thickness, whether the part is complete, and the like of the part are checked in detail. And summarizing the inspection results to obtain the detection result of the vehicle to be detected.
In one or more embodiments of the present invention, after acquiring the scanned image of the vehicle to be detected, the method may further include: according to the material category and the thickness of a part in the vehicle to be detected, performing color rendering on the part; wherein, different material categories correspond to different color categories, and the same material categories with different thicknesses correspond to the same color categories with different shades.
In practical applications, the scanning with the X-ray device may obtain a whole image of the vehicle, in which the components are connected or overlapped, and in order to better distinguish the components, the components may be rendered according to material type and thickness. Specifically, parts belonging to the same material category are rendered in the same color, for example, organic is rendered orange, metal is rendered blue, and mixture is rendered green. Further, if the material types are the same, the color depth can be adjusted according to the different thicknesses. For example, the thick steel sheet is dark blue, and the thin steel sheet is light blue. Through the rendering steps, each component can be clearly seen.
In one or more embodiments of the present invention, before determining the difference region, the method may further include: identifying a target collation point in the first image; and aligning a preset calibration point in the second image with the target calibration point.
For example, the preset calibration point is set in the preselected setting second image, such as setting four vertexes and a center point of the second image as the preset calibration point. After the first image is acquired, four vertexes and a central point in the first image are locked to be used as target calibration points. Further, the target calibration point is aligned with the preset calibration point, so that the difference area of the image can be searched subsequently.
In one or more embodiments of the present invention, the identifying the difference type of the difference region may further include: and if the difference area lacks a part graph, determining that the vehicle to be detected lacks a corresponding part.
In practical application, the first image and the second image are compared one by one through pixel points. And forming a difference area by the pixel points with the difference. After the difference area is determined, if the difference area in the first image is not provided with the graph and the difference area in the second image is provided with the graph, the fact that the difference area of the vehicle to be detected is lack of the component is confirmed.
In one or more embodiments of the present invention, if it is determined that a component corresponding to the difference region is not absent, the identifying the difference type of the difference region may further include: and if the color types of the different areas are different, determining that the material types of the parts corresponding to the vehicle to be detected are different.
As can be seen from the foregoing, parts of different material classes are rendered into different color classes. And if the abnormal area is found to have the component after the abnormal area is determined, further identifying the color type of the abnormal area and marking the identification result.
In one or more embodiments of the present invention, if the material types of the difference regions are the same, the identifying the difference types of the difference regions may further include: and if the different areas have the same color types with different depths, determining that the thicknesses of the corresponding parts of the vehicle to be detected are different.
In practical applications, if it is determined that the color types corresponding to the different regions are the same, it may be further determined whether the color shades of the different regions are the same. For example, if the color of the difference region in the first image is light blue and the color of the difference region in the second image is dark blue, it is determined that the thickness of the part in the difference region in the vehicle to be detected is thinner than that of the part in the standard vehicle.
It should be noted that the thickness of the component can be detected only when the component is found in the difference region and the material type of the component is the same.
In one or more embodiments of the present invention, the identifying the difference type of the difference region may specifically include: and if the part graph of the difference area has the linear trace, determining that the part corresponding to the vehicle to be detected has the crack.
In practice, some parts may have cracks. Therefore, after the differential area is locked, the presence of the component is determined, and then the crack inspection can be performed. In other words, the step of performing the crack inspection on the difference region may be performed simultaneously with the step of performing the material type detection on the difference region, or may be performed after the thickness detection is completed.
In one or more embodiments of the present invention, the method may further include: determining the fraction score, the material category difference score, the thickness difference score and the crack score.
It is easy to understand that after the difference types corresponding to the difference regions are determined, the parts are scored according to the difference types, and further, the value of the vehicle to be detected is determined according to the values of the missing parts, the values of the material class differences, the values of the thickness differences and the values of the cracks, which respectively correspond to the difference classes.
Based on the same idea, as shown in fig. 2, an embodiment of the present invention further provides a vehicle detection apparatus, including:
the acquisition module 21 is used for acquiring a first image of a vehicle to be detected;
a difference determination module 22, configured to compare the first image with a second image of a standard vehicle, and determine a difference region;
the detection module 23 is configured to identify a difference type of the difference region to determine a detection result of the vehicle to be detected.
Further, still include: the rendering module 24 is used for rendering colors of the parts in the vehicle to be detected according to the material types and thicknesses of the parts; wherein, different material categories correspond to different color categories, and the same material categories with different thicknesses correspond to the same color categories with different shades.
Further, a difference determination module 22 for identifying a target calibration point in the first image;
and aligning a preset calibration point in the second image with the target calibration point.
Further, the detecting module 23 determines that the vehicle to be detected lacks a corresponding component if the difference region lacks a component pattern.
Further, if it is determined that the component corresponding to the difference region is not absent, the identifying the difference type of the difference region includes:
and if the color types of the different areas are different, determining that the material types of the parts corresponding to the vehicle to be detected are different.
Further, if the material types of the difference regions are the same, the identifying the difference types of the difference regions includes:
and if the different areas have the same color types with different depths, determining that the thicknesses of the corresponding parts of the vehicle to be detected are different.
Further, the identifying the difference type of the difference region includes:
and if the part graph of the difference area has the linear trace, determining that the part corresponding to the vehicle to be detected has the crack.
Further, still include: determining the fraction score, the material category difference score, the thickness difference score and the crack score.
Further, still include: and determining the value of the vehicle to be detected according to the missing part score, the material class difference score, the thickness difference score and the crack score which respectively correspond to each difference class.
Based on the same idea, as shown in fig. 3, an embodiment of the present invention further provides an electronic device, which includes a processor 31, a memory 32, and the memory 32 is configured to store one or more computer instructions, where the one or more computer instructions, when executed by the processor 31, implement:
acquiring a first image of a vehicle to be detected;
comparing the first image with a second image of a standard vehicle to determine a difference area;
and identifying the difference type of the difference area to determine the detection result of the vehicle to be detected.
In addition, an embodiment of the present invention provides a computer storage medium for computer software instructions for a server, which contains a program for executing the vehicle detection method in the above-described method embodiment shown in fig. 1.
Based on the above embodiments, it can be understood that the vehicle to be detected is subjected to image scanning, the scanned image obtained by scanning is compared with the second image obtained in advance, and the difference points in the scanned image and the second image are searched. And further, according to a preset identification rule, identifying the difference types of the difference points, generating identification results corresponding to the difference points, and estimating the value of the vehicle to be detected according to the difference identification results. Based on the scheme, when the value of the vehicle to be estimated is evaluated, the detection of the conditions of all parts in the vehicle can be realized without disassembling the vehicle, the detection efficiency can be effectively improved, and the accuracy of the detection result is improved.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by adding a necessary general hardware platform, and of course, can also be implemented by a combination of hardware and software. With this understanding in mind, the above-described aspects and portions of the present technology which contribute substantially or in part to the prior art may be embodied in the form of a computer program product, which may be embodied on one or more computer-usable storage media having computer-usable program code embodied therein, including without limitation disk storage, CD-ROM, optical storage, and the like.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable resource updating apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable resource updating apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable resource updating apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable resource updating apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A vehicle detection method, characterized in that the method comprises:
acquiring a first image of a vehicle to be detected;
comparing the first image with a second image of a standard vehicle to determine a difference area;
identifying the difference type of the difference area to determine the detection result of the vehicle to be detected;
after the first image is scanned by the vehicle to be detected, the method further comprises the following steps:
according to the material category and the thickness of a part in the vehicle to be detected, performing color rendering on the part; wherein, different material classes correspond to different color classes, and the same material classes with different thicknesses correspond to the same color classes with different depths;
if the material types of the difference regions are the same, the identifying the difference types of the difference regions includes: if the different areas have the same color types with different depths, determining that the thicknesses of the parts corresponding to the vehicle to be detected are different;
wherein, the determining mode of the difference region comprises the following steps:
comparing the first image with the second image one by one, and marking pixel points according to the color types or the differences of the color shades of the pixel points; and forming a difference region based on the plurality of marked pixel points.
2. The method of claim 1, wherein prior to determining the difference region, further comprising:
identifying a target collation point in the first image;
and aligning a preset calibration point in the second image with the target calibration point.
3. The method of claim 1, wherein the identifying the difference type of the difference region comprises:
and if the difference area lacks a part graph, determining that the vehicle to be detected lacks a corresponding part.
4. The method of claim 3, wherein identifying the difference type of the difference region if it is determined that the component corresponding to the difference region is not missing comprises:
and if the color types of the different areas are different, determining that the material types of the parts corresponding to the vehicle to be detected are different.
5. The method according to claim 1 or 3, wherein the identifying the difference type of the difference region comprises:
and if the part graph of the difference area has the linear trace, determining that the part corresponding to the vehicle to be detected has the crack.
6. The method of claim 5, further comprising: determining the fraction score, the material category difference score, the thickness difference score and the crack score.
7. The method of claim 6, further comprising:
and determining the value of the vehicle to be detected according to the missing part score, the material class difference score, the thickness difference score and the crack score which respectively correspond to each difference class.
8. A vehicle detection apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring a first image of a vehicle to be detected;
the difference determining module is used for comparing the first image with a second image of a standard vehicle to determine a difference area;
the detection module is used for identifying the difference type of the difference area so as to determine the detection result of the vehicle to be detected; the rendering module is used for rendering colors of the parts in the vehicle to be detected according to the material types and the thicknesses of the parts; wherein, different material classes correspond to different color classes, and the same material classes with different thicknesses correspond to the same color classes with different depths;
a detection module, configured to identify the difference type of the difference region if the material types of the difference region are the same, including: if the different areas have the same color types with different depths, determining that the thicknesses of the parts corresponding to the vehicle to be detected are different;
wherein, the determining mode of the difference region comprises the following steps:
comparing the first image with the second image one by one, and marking pixel points according to the color types or the differences of the color shades of the pixel points; and forming a difference region based on the plurality of marked pixel points.
9. An electronic device comprising a processor, a memory to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement:
acquiring a first image of a vehicle to be detected;
comparing the first image with a second image of a standard vehicle to determine a difference area;
identifying the difference type of the difference area to determine the detection result of the vehicle to be detected;
after the first image is obtained and scanned, the method further comprises the following steps: according to the material category and the thickness of a part in the vehicle to be detected, performing color rendering on the part; wherein, different material classes correspond to different color classes, and the same material classes with different thicknesses correspond to the same color classes with different depths;
if the material types of the difference regions are the same, the identifying the difference types of the difference regions includes: if the different areas have the same color types with different depths, determining that the thicknesses of the parts corresponding to the vehicle to be detected are different;
wherein, the determining mode of the difference region comprises the following steps:
comparing the first image with the second image one by one, and marking pixel points according to the color types or the differences of the color shades of the pixel points; and forming a difference region based on the plurality of marked pixel points.
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| CN112801987B (en) * | 2021-02-01 | 2022-11-08 | 上海万物新生环保科技集团有限公司 | Mobile phone part abnormity detection method and equipment |
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| CN102455305B (en) * | 2010-11-01 | 2014-06-18 | 北京中盾安民分析技术有限公司 | Image processing method used by device for detecting hidden matters on human body by using dual-energy X-rays |
| CN105784732B (en) * | 2014-12-26 | 2019-02-22 | 同方威视技术股份有限公司 | Inspection method and inspection system |
| CN108088686B (en) * | 2017-12-21 | 2020-11-20 | 驭势科技（浙江）有限公司 | Test simulation method and device, computer storage medium and electronic equipment |
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| CN102323070A (en) * | 2011-06-10 | 2012-01-18 | 北京华兴致远科技发展有限公司 | Method and system for detecting abnormality of train |
| CN106503121A (en) * | 2016-10-19 | 2017-03-15 | 公安部第三研究所 | A kind of description method of X-ray safety check image and system |
| CN107884426A (en) * | 2017-12-26 | 2018-04-06 | 上海够得实业有限公司 | A kind of Novel button detection device |
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