CN118225448A

CN118225448A - Vehicle detection method, apparatus, device, storage medium, and program product

Info

Publication number: CN118225448A
Application number: CN202211635381.1A
Authority: CN
Inventors: 高鸿海
Original assignee: Beijing Youzhuju Network Technology Co Ltd
Current assignee: Beijing Youzhuju Network Technology Co Ltd
Priority date: 2022-12-19
Filing date: 2022-12-19
Publication date: 2024-06-21

Abstract

The present disclosure relates to a vehicle collision accident determination method, apparatus, device, storage medium, and program product, the method comprising: aiming at a vehicle part in a vehicle to be detected, acquiring paint film data of the vehicle part; determining a paint type of the vehicle part based on the paint film data; counting the number of vehicle parts with paint types being repair types, and determining the position relation between the vehicle parts with paint types being repair types; and determining whether the vehicle to be detected is an accident vehicle or not based on the number and the position relation of the vehicle parts of which the paint type is a repair type. According to the method and the device, whether the paint type of the vehicle part is repaired or not is judged through the paint film data of the vehicle part, the number and the position relation of the vehicle parts with the repaired paint in the whole vehicle are counted, whether the vehicle has a collision accident or not is further determined according to the number and the position relation of the vehicle parts with the repaired paint, accuracy of vehicle detection and evaluation is improved, and detection service timeliness is improved.

Description

Vehicle detection method, apparatus, device, storage medium, and program product

Technical Field

The present disclosure relates to the field of computer processing technology, and in particular, to a vehicle detection method, apparatus, device, storage medium, and program product.

Background

With the continuous improvement of public living standards and the change of consumption concepts, more and more people own vehicles, so that the development of the second-hand vehicle market is promoted to be extremely rapid. At present, information in the second-hand vehicle market is opaque, and the vehicle condition is still an industry pain point.

The detection of vehicle outward appearance covering part is the important part that the second hand car condition detected, and at present inspector is in the in-process that carries out vehicle outward appearance covering part and detects the paint film thickness on vehicle outward appearance paint surface through the paint film appearance, then judges whether this vehicle outward appearance part exists the condition of spraying paint based on paint film thickness according to manual experience, and then judges whether this vehicle has taken place the collision accident.

The judgment process is based on accumulation of experience of the detection personnel, and subjective factors are strong. The detection accuracy of the inexperienced detection personnel is lower, and the judgment time is longer.

Disclosure of Invention

In order to solve the technical problems, the embodiments of the present disclosure provide a vehicle detection method, apparatus, device, storage medium, and program product, which determine whether a paint type of a vehicle part is repaired according to paint film data of the vehicle part, and count the number and positional relationship of the vehicle parts with the repaired paint in the whole vehicle, so as to determine whether a collision accident occurs to the vehicle according to the number and positional relationship of the vehicle parts with the repaired paint, improve accuracy of vehicle detection and evaluation, and improve detection service timeliness.

In a first aspect, an embodiment of the present disclosure provides a vehicle detection method, including:

For a vehicle part in a vehicle to be detected, acquiring paint film data of the vehicle part;

determining a paint type of the vehicle part based on the paint film data;

Counting the number of the vehicle parts of which the paint types are repair types, and determining the position relation between the vehicle parts of which the paint types are repair types;

and determining whether the vehicle to be detected is an accident vehicle or not based on the number of vehicle parts of which the paint type is repair type and the position relation.

In a second aspect, an embodiment of the present disclosure provides a vehicle detection apparatus including:

the paint film data acquisition module is used for acquiring paint film data of a vehicle part aiming at the vehicle part in the vehicle to be detected;

a paint type determination module for determining a paint type of the vehicle component based on the paint film data;

the quantity and relation determining module is used for counting the quantity of the vehicle parts of which the paint types are repair types and determining the position relation among the vehicle parts of which the paint types are repair types;

and the accident vehicle determining module is used for determining whether the vehicle to be detected is an accident vehicle or not based on the number and the position relation of the vehicle parts of which the paint types are repair types.

In a third aspect, an embodiment of the present disclosure provides an electronic device, including:

one or more processors;

a storage means for storing one or more programs;

The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the vehicle detection method as set forth in any one of the first aspects above.

In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the vehicle detection method according to any one of the first aspects described above.

In a fifth aspect, embodiments of the present disclosure provide a computer program product comprising a computer program or instructions which, when executed by a processor, implement a vehicle detection method as described in any one of the first aspects above.

Embodiments of the present disclosure provide a vehicle detection method, apparatus, device, storage medium, and program product, the method including: for a vehicle part in a vehicle to be detected, acquiring paint film data of the vehicle part; determining a paint type of the vehicle part based on the paint film data; counting the number of the vehicle parts of which the paint types are repair types, and determining the position relation between the vehicle parts of which the paint types are repair types; and determining whether the vehicle to be detected is an accident vehicle or not based on the number of vehicle parts of which the paint type is repair type and the position relation. According to the method and the device, whether the paint type of the vehicle part is repaired or not is judged through the paint film data of the vehicle part, the number and the position relation of the vehicle parts with the repaired paint in the whole vehicle are counted, whether the vehicle has a collision accident or not is further determined according to the number and the position relation of the vehicle parts with the repaired paint, accuracy of vehicle detection and evaluation is improved, and detection service timeliness is improved.

Drawings

The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.

FIG. 1 is a schematic illustration of a vehicle detection scenario in accordance with an embodiment of the present invention;

FIG. 2 is a flow chart of a vehicle detection method in an embodiment of the disclosure;

FIG. 3 is a schematic view of a vehicle detection apparatus according to an embodiment of the present disclosure;

fig. 4 is a schematic structural diagram of an electronic device in an embodiment of the disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.

The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.

It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

Before explaining the embodiments of the present disclosure in further detail, terms and terminology involved in the embodiments of the present disclosure are explained, and the terms and terminology involved in the embodiments of the present disclosure are applicable to the following explanation.

In response to a condition or state that is used to represent the condition or state upon which the performed operation depends, the performed operation or operations may be in real-time or with a set delay when the condition or state upon which it depends is satisfied; without being specifically described, there is no limitation in the execution sequence of the plurality of operations performed.

With the continuous improvement of public living standards and the change of consumption concepts, more and more people own vehicles, so that the development of the second-hand vehicle market is promoted to be extremely rapid. Many practitioners or purchasers of a second-hand vehicle currently associated with the second-hand vehicle wish to be able to understand the vehicle condition of the second-hand vehicle.

For example: the second-hand car purchaser can worry about the car condition, and is afraid of buying the major accident car; the second-hand vehicle manufacturer lacks professional automobile knowledge and experience and worries about purchasing the major accident vehicle at high price; the insurance company wants to know the vehicle condition and uses the vehicle condition as the basis for making the premium and providing the delay insurance; the loan company wants to know the vehicle condition as the basis of whether to loan and loan amount, etc.

The detection of vehicle outward appearance covering part is the important part that the second hand car condition detected, and at present inspector is in the in-process that carries out vehicle outward appearance covering part and detects the paint film thickness on vehicle outward appearance paint surface through the paint film appearance, clicks vehicle outward appearance paint surface through the paint film appearance, detects the paint film thickness of this vehicle part, obtains the standard range of the paint film thickness of vehicle and carries out the reference, then needs to go according to inspector's manual experience and judge whether there is the condition of spraying paint in a certain spare part of vehicle, and then judge whether this vehicle has had collision accident according to the condition of spraying paint part.

The judgment process is based on accumulation of experience of detection personnel, subjective factors are strong, and because experience of different detection personnel is uneven, and metal putty used in paint spraying of part of vehicle metal plates causes that whether appearance paint is in a process of detecting paint film thickness of a vehicle is judged, and standardization and unification cannot be achieved. In addition, paint film data of a vehicle can have certain errors due to the air pressure of paint spraying, the proportion of diluent and the process in the process of maintaining in a host factory, so that standard data are difficult, the accuracy of detection for inexperienced detection personnel is low, and the judgment time is long.

To solve the above technical problems, an embodiment of the present disclosure provides a vehicle detection method, including: for a vehicle part in a vehicle to be detected, acquiring paint film data of the vehicle part; determining a paint type of the vehicle part based on the paint film data; counting the number of the vehicle parts of which the paint types are repair types, and determining the position relation between the vehicle parts of which the paint types are repair types; and determining whether the vehicle to be detected is an accident vehicle or not based on the number of vehicle parts of which the paint type is repair type and the position relation.

According to the method and the device, whether the paint type of the vehicle part is repaired or not is judged through the paint film data of the vehicle part, the number and the position relation of the vehicle parts with the repaired paint in the whole vehicle are counted, whether the vehicle has a collision accident or not is further determined according to the number and the position relation of the vehicle parts with the repaired paint, accuracy of vehicle detection and evaluation is improved, and detection service timeliness is improved.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. It should be noted that the same reference numerals in different drawings will be used to refer to the same elements already described.

Fig. 1 is a system configuration diagram that may be used to implement the vehicle detection method provided by embodiments of the present disclosure. As shown in fig. 1, the system 100 may include a user terminal 110, a network 120, a server 130, and a database 140. For example, the system 100 may be used to implement the vehicle detection method described in any of the embodiments of the present disclosure.

It is understood that user terminal 110 may be any other type of electronic device capable of performing data processing, which may include, but is not limited to: mobile handsets, sites, units, devices, multimedia computers, multimedia tablets, internet nodes, communicators, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, personal Communications Systems (PCS) devices, personal navigation devices, personal Digital Assistants (PDAs), audio/video players, digital cameras/video cameras, locating devices, television receivers, radio broadcast receivers, electronic book devices, gaming devices, or any combination thereof, including accessories and peripherals for these devices, or any combination thereof.

The user may operate through an application installed on the user terminal 110, the application transmitting data input by the user to the server 130 through the network 120, and the user terminal 110 may also receive the data transmitted by the server 130 through the network 120.

In an application scenario of the present disclosure, a user may operate through an application program installed on the user terminal 110, where the application program transmits a vehicle detection instruction to the server 130 through the network 120, where the vehicle detection instruction carries paint film data, and after the server 130 receives the vehicle detection instruction, the vehicle detection method provided by the embodiment of the present disclosure is executed to obtain a detection result of whether the vehicle to be detected is an accident vehicle, and the server 130 sends the detection result of whether the vehicle to be detected is the accident vehicle to the application program through the network 120, so that the user terminal 110 may display the detection result.

In another application scenario of the present disclosure, a user may operate through an application program installed on the user terminal 110, execute the vehicle detection method provided in the embodiment of the present disclosure at the local end of the user terminal 110, obtain a detection result of whether the vehicle to be detected is an accident vehicle, and display the detection result.

The embodiments of the present disclosure are not limited to the hardware system and the software system of the user terminal 110, for example, the user terminal 110 may be based on an ARM, an X86, or the like, may be provided with an input/output device such as a camera, a touch screen, a microphone, or the like, and may be operated with an operating system such as Windows, iOS, linux, android, hong OS, or the like.

The user terminal 110 may implement the vehicle detection method provided in the embodiments of the present disclosure by running a process or thread. In some examples, user terminal 110 may perform the vehicle detection method using its built-in application. In other examples, user terminal 110 may perform the vehicle detection method by invoking an application program stored external to user terminal 110.

Network 120 may be a single network or a combination of at least two different networks. For example, network 120 may include, but is not limited to, one or a combination of several of a local area network, a wide area network, a public network, a private network, and the like. The network 120 may be a computer network such as the Internet and/or various telecommunications networks (e.g., 3G/4G/5G mobile communication networks, W IFI, bluetooth, zigBee, etc.), to which embodiments of the present disclosure are not limited.

The server 130 may be a single server, or a group of servers, or a cloud server, with each server within the group of servers being connected via a wired or wireless network. A server farm may be centralized, such as a data center, or distributed. The server 130 may be local or remote. The server 130 may communicate with the user terminal 110 through a wired or wireless network. Embodiments of the present disclosure are not limited to the hardware system and software system of server 130.

Database 140 may refer broadly to a device having a storage function. The database 140 is mainly used to store various data utilized, generated, and outputted by the user terminal 110 and the server 130 in operation. Database 140 may be local or remote. The database 140 may include various memories, such as random access Memory (Random Access Memory, RAM), read Only Memory (ROM), and the like. The above-mentioned storage devices are merely examples and the storage devices that may be used by the system 100 are not limited in this regard. Embodiments of the present disclosure are not limited to hardware systems and software systems of database 140, and may be, for example, a relational database or a non-relational database.

Database 140 may be interconnected or in communication with server 130 or a portion thereof via network 120, or directly with server 130, or a combination thereof.

In some examples, database 140 may be a stand-alone device. In other examples, database 140 may also be integrated in at least one of user terminal 110 and server 130. For example, the database 140 may be provided on the user terminal 110 or on the server 130. For another example, the database 140 may be distributed, with one portion being provided on the user terminal 110 and another portion being provided on the server 130.

Fig. 2 is a flowchart of a vehicle detection method in an embodiment of the disclosure, which may be suitable for use in detecting a second-hand vehicle, where the method may be performed by a vehicle detection device, which may be implemented in software and/or hardware, and the vehicle detection method may be performed by the user terminal 110 described in fig. 1 or the server 130 described in fig. 1.

As shown in fig. 2, the vehicle detection method provided in the embodiment of the present disclosure mainly includes steps S101 to S104.

S101, aiming at a vehicle part in a vehicle to be detected, acquiring paint film data of the vehicle part.

In the embodiment of the present disclosure, the vehicle to be detected refers to a vehicle that a user wants to detect and evaluate whether an oversized collision accident occurs, and further, the vehicle to be detected is a second-hand vehicle, i.e., a vehicle that has been used after being taken off-line. The user may be any one of a second-hand vehicle purchaser, a worker of a second-hand vehicle manufacturer, a worker of an insurance company, a worker of a loan company, and the like, which uses the user terminal 110.

In one embodiment of the present disclosure, the above-mentioned vehicle component may be understood as a relevant part constituting a vehicle, and optionally, the above-mentioned vehicle component is an exterior cover of the vehicle, for example: an engine compartment cover, a trunk lid, four doors (left front door, left rear door, right front door, right rear door), four fenders (left front fender, left rear fender, right front fender, right rear fender), a roof, a front bumper, a rear bumper, and the like.

In one embodiment of the present disclosure, in order to determine the paint condition of a vehicle to be detected, the user terminal 110 needs to acquire paint film data of a vehicle part. It should be noted that, typically, there is more than one vehicle component to be detected by one vehicle to be detected. Therefore, it is necessary to acquire paint film data corresponding to all the exterior covers of the vehicle to be detected. Wherein the paint film data may refer to the paint film thickness of the vehicle part of the vehicle to be detected.

In one embodiment of the present disclosure, the acquisition of paint film data of the vehicle section may be that the user terminal 110 is in communication connection with the paint film detector, and the paint film data of the vehicle section is acquired from the paint film detector directly through the communication connection. It is also possible that after the paint film detector detects paint film data of the vehicle section, the inspector looks up the paint film data and inputs it to the user terminal 110, and the user terminal 110 acquires the paint film data input by the inspector.

In one embodiment of the present disclosure, each vehicle to be tested will correspond to a plurality of paint film data, one for each vehicle section. For example: the front left door corresponds to paint film data 1, the engine hatch corresponds to paint film data 2, the front left fender corresponds to paint film data 3, and so on. I.e. each vehicle part will detect its corresponding paint film data.

In one embodiment of the present disclosure, a inspector selects a currently-to-be-inspected vehicle part in the user terminal 110, and then detects paint film data of the currently-to-be-inspected vehicle part using a paint film detector from which the user terminal 110 acquires the paint film data and establishes a correspondence of the currently-to-be-inspected vehicle part and the paint film data. The above-described operation is performed for each outer cover piece in the vehicle to be detected.

For example: and (3) a detector selects that the vehicle part to be detected currently is a left front door in the vehicle detection equipment, and then the paint film detector is used for detecting paint film data 1 of the paint film data of the left front door, the vehicle detection equipment acquires the paint film data 1 from the paint film detector, and a corresponding relation between the left front door and the paint film data 1 is established.

In one embodiment of the present disclosure, the acquiring paint film data of the vehicle part includes: respectively performing paint film detection on a plurality of detection points in the vehicle part to obtain a plurality of sampled paint film data, wherein the plurality of detection points are positioned at different positions in the same vehicle part; the average value of the plurality of sampled paint film data is taken as the paint film data of the vehicle part.

In the embodiment of the disclosure, since the vehicle part may be only one area of the parts to be painted or sheet metal in the repair process, the paint film detector may be caused to detect in the detection process, and the detected paint film data is in the paint film standard range due to the fact that the detection point selects the area which is not repaired, so that an error occurs in judging whether the vehicle part is repaired or not.

In the embodiment of the disclosure, a plurality of detection points are selected for detection aiming at the same vehicle part so as to ensure that the detected paint film data can correctly reflect whether the vehicle part is repaired or not.

Optionally, the plurality of detection points may be 5 detection points, and the detection points may be an upper left region, a lower left region, an upper right region, a lower right region and a middle region of the vehicle part, and each region selects one detection point to detect paint film data, so as to obtain 5 sampled paint film data. An average of 5 sampled paint film data was calculated and taken as paint film data for the vehicle part.

It should be noted that, in the embodiment of the present disclosure, 5 monitoring points are taken as an example for illustration, and the number of the monitoring points may be set according to actual situations.

S102, determining the paint type of the vehicle part based on the paint film data.

If the vehicle is crashed or scratched, repair staff can spray paint and repair the vehicle when repairing the vehicle, and even repair the vehicle by using metal plates. The thickness of the paint in the repair area of the vehicle component may change. Even if the vehicle parts are replaced, the paint film thickness of the replaced vehicle parts is different from that of the original factory vehicle parts. Thus, paint film data for a vehicle part may reflect whether the vehicle part has been repaired. Wherein the paint types include repaired and unrepaired types.

In the embodiment of the disclosure, the repairing method comprises repairing by any one or more repairing modes such as paint spraying repairing, sheet metal repairing, metal putty repairing, component replacement and the like. Unrepaired refers to vehicle components that have not been repaired by any painting.

When the vehicle parts are severely damaged and need to be replaced, even if the factory parts are replaced, the factory parts are not painted when leaving the factory, and the factory parts are replaced after being painted by repair staff after reaching an automobile repair shop. Since the original factory parts are painted by repairmen, the paint film thickness is different from the standard range of the paint film, and therefore, whether the vehicle parts are replaced or not can be judged through the paint film data. The paint type of the replaced vehicle component is determined as a repair class.

In the embodiment of the disclosure, paint film data is compared with a preset paint film standard range, and if the paint film data is within the preset paint film standard range, the vehicle part is indicated to be a normal part without repair, namely, the paint type of the vehicle part is an unrepaired type. And if the paint film data is out of the preset paint film standard range, indicating that the vehicle part is repaired, namely the paint type of the vehicle part is repair type.

In one embodiment of the present disclosure, the paint film standard ranges described above may be determined from paint film data at the time of shipment of the vehicle. Further, the paint film standard range is that all vehicles correspond to one paint film standard range, namely, a unified paint film standard range is set.

In one embodiment of the present disclosure, paint film thickness may vary from vehicle to vehicle due to different vehicle systems, different paint spray techniques employed by vehicle manufacturers. Different paint film standard ranges are set according to the automobile system, for example, the set paint film standard range of the solar automobile system is 80-130 micrometers, the set paint film standard range of the German automobile system is 100-150 micrometers, the set paint film standard range of the American automobile system is 150-200 micrometers, and the like.

In an embodiment of the present disclosure, the determining a paint type of the vehicle part based on the paint film data includes: acquiring vehicle information of the vehicle to be detected; acquiring a paint film standard range corresponding to the vehicle to be detected based on the vehicle information; if the paint film data is within the paint film standard range, determining that the paint type of the vehicle part is a repair type; if the paint film data of the vehicle part is not within the paint film standard range, determining that the paint type of the vehicle part is an unrepaired type.

The vehicle information includes a vehicle system to which the vehicle belongs, and the vehicle information can be manually input by a user or can be determined by a vehicle identification code and a vehicle brand.

Specifically, a vehicle train of a vehicle to be detected is obtained, if the vehicle to be detected is a De train, the De train sets a paint film standard range of 100-150 micrometers, if paint film data of a vehicle part is within 100-150 micrometers, the paint type of the vehicle part is determined to be an unrepaired type, and if the paint film data of the vehicle part is outside 100-150 micrometers, namely the paint film data is less than 100 millimeters or greater than 150 millimeters, the paint type of the vehicle part is determined to be a repaired type.

In the disclosed embodiments, paint film thickness is different even for different vehicle parts of the same vehicle system. And setting corresponding paint film standard ranges for different vehicle parts of different train.

Specifically, the part types of the vehicle system and the vehicle parts of the vehicle to be detected are obtained, and the paint film standard range corresponding to the vehicle to be detected is obtained based on the vehicle system and the part types; if the paint film data of the vehicle part is within the paint film standard range, determining that the vehicle part is a normal part; if the paint film data of the vehicle part is not within the paint film standard range, determining that the vehicle part is a repair part.

In one embodiment of the present disclosure, the determining the paint type of the vehicle part based on the paint film data includes: judging whether paint film data of the vehicle part are in a paint film standard range or not; determining maximum sampled paint film data and minimum sampled paint film data from the plurality of sampled paint film data if the paint film data of the vehicle section is within the paint film standard range; calculating the difference value of the maximum sampled paint film data and the minimum sampled paint film data; if the difference is greater than a preset value, determining that the paint type of the vehicle part is a repair type; and if the difference value is smaller than or equal to a preset value, determining that the paint type of the vehicle part is an unrepaired type.

In the disclosed embodiments, there may be a special repair mode, i.e., metal putty repair, in the repair of the surface paint film of the vehicle component. The metal putty repair is to scrape metal putty in the damaged area of the vehicle part and to repair the metal putty after the metal putty is dried. Because the metal putty is scraped and then sprayed with paint, in the paint surface airing process, the flow of the liquid paint can cause thicker paint film thickness in certain areas of the vehicle part and thinner paint film thickness in certain areas. In the scheme employing the average value of the plurality of detections as paint film data, the average value of the plurality of sampled paint film data may be made to be within the paint film standard range, and therefore, the paint type of the vehicle part repaired with the metal putty may be judged as an unrepaired type, resulting in erroneous judgment. .

To avoid the above, in the embodiment of the present disclosure, the difference between the maximum sampled paint film data and the minimum sampled paint film data is calculated; and if the difference value is larger than a preset value, determining that the paint type of the vehicle part is repair type. And if the difference value is smaller than or equal to a preset value, determining that the paint type of the vehicle part is an unrepaired type.

In the embodiment of the disclosure, the metal putty is used for paint spraying, and the paint film has fluidity in the process from paint spraying to paint film airing, so that the sampled paint film data obtained at different detection points of the same vehicle part are different. For example: the paint film data of the lower part is larger than that of the upper part after paint spraying because the paint film has fluidity, so that the paint film data floats greatly in a normal range, the vehicle part is judged to be specially repaired, and the paint type of the vehicle part is judged to be repair type.

In an embodiment of the disclosure, a paint film standard range determination method is provided. Mainly comprises the following steps: and obtaining n detection samples of the vehicle paint surface detection, obtaining a vehicle system corresponding to each detection sample, and determining a paint film standard range corresponding to the vehicle system according to the n detection samples and the vehicle system corresponding to the detection samples.

Specifically, the detection device acquires n detection samples. The n detection samples are histories of the detection of the paint conditions of the vehicle. A test sample includes paint film data and a vehicle part test result. A test sample is a record of the detection of paint surfaces of a vehicle component of a vehicle. Therefore, the paint film data in the detection sample refers to the paint film thickness of the detected vehicle part, and the detection result of the vehicle part in the detection sample is whether the detected vehicle part is repaired or not. Different vehicle part test results correspond to different paint film thicknesses, and different paint film thicknesses may correspond to the same paint film test result. For example, paint film thickness on a car roof for two years is typically 90 to 100 microns without scratch and repair recording. Then, the paint film thicknesses in the range of 90 micrometers to 100 micrometers all correspond to the same paint film detection result. Optionally, the vehicle component detection result includes a repair occurring or not occurring.

The detection device needs to ensure that accurate detection standards can be generated from the detection samples. Therefore, it is necessary to ensure the reliability and accuracy of the data of the detection sample. Since the detection sample is a history of paint detection, and there may be a deviation or error in paint detection in the history, the detection apparatus needs to eliminate the influence caused by the detection sample having the deviation or error. In this regard, the number n of detection samples by the detection device is a positive integer greater than a preset number. The preset number may be set by a technician according to practical experience, for example, the preset number is 10000, so as to reduce the influence of deviation or wrong detection samples on the detection standard. According to the data statistics principle, the larger the number of detection samples, the smaller the influence of the deviation or error detection samples on the sample population. Therefore, the detection device acquires a greater than preset number of detection samples to reduce the influence of deviation or false detection samples on the detection standard. Alternatively, for the detection sample acquired by the detection apparatus, a technician may perform a test in advance to remove the detection sample in which there is a deviation or error, thereby further improving the accuracy of the detection standard.

In this regard, the detection apparatus needs to determine the vehicle train to which each detection sample corresponds. Thus, the detection apparatus acquires the vehicle train corresponding to each detection sample. The vehicle train corresponding to each detection sample is recorded by a detection person in the detection process and stored in the detection equipment. When needed, the detection equipment directly acquires the corresponding vehicle train.

The detection device generates a plurality of paint standard ranges according to the n detection samples. Each paint standard range corresponds to a vehicle train. Because paint spray is different for different vehicle systems, paint film thickness is different for different vehicle systems. The detection equipment generates paint detection standards by carrying out data statistics on n detection samples, and determining the range of paint film thickness corresponding to the same train as the paint film standard range of the train.

Furthermore, with the gradual increase of the detection samples of the same type of vehicle system, the standard range of the paint film can be gradually narrowed and the accuracy is improved.

S103, counting the number of the vehicle parts with the paint type being repair type, and determining the position relation between the vehicle parts with the paint type being repair type.

In the embodiment of the disclosure, after the vehicle part is determined to be of the repair type, the vehicle part is marked, and after paint film data of all the vehicle parts are judged to be finished, the number of the vehicle parts marked as the repair type is counted.

In the embodiment of the disclosure, the number of repair type vehicle parts is counted by adopting a counter mode, after the vehicle parts are determined to be of repair type, the counter is increased by 1, and after a paint film detection completion instruction input by a detection person is received, the number of the counter is used as the number of repair type vehicle parts.

The positional relationship between the vehicle components can be understood as whether the vehicle components are adjacent and as the distance between the vehicle components. For example: the engine compartment cover and the left front fender are in an adjacent positional relationship, and the left front fender and the front bumper are in an adjacent positional relationship. The close positional relationship means that the distance between the two vehicle components is within a preset range. Wherein the preset range may be set according to a specific vehicle component. For example: the distance between the front fender and the rear door is the width of the front door. If the vehicle component is a front fender and a rear door, the preset range is the width of the front door.

S104, determining whether the vehicle to be detected is an accident vehicle or not based on the number of the vehicle parts of which the paint type is repair type and the position relation.

In the embodiment of the disclosure, the accident vehicle refers to a vehicle with structural damage when an oversized collision accident occurs. In particular, the presence of multiple components in the exterior cover of a vehicle that are repaired indicates that an excessive crash event may occur in the vehicle with a greatly increased likelihood of structural damage.

In one embodiment of the present disclosure, the root

Determining whether the vehicle to be detected is an accident vehicle based on the number and the positional relationship of the vehicle components of which the paint type is a repair type, including: if the position relation between the vehicle components with the paint type being repair type meets the preset requirement and the number of the vehicle components with the paint type being repair type is larger than or equal to a preset value, determining that the vehicle to be detected is an accident vehicle, wherein the position relation between the vehicle components meets the preset requirement, namely that the distance between the vehicle components is within a preset range; and if the position relation between the vehicle parts of which the paint types are repair types does not meet the preset requirement or the number of the vehicle parts of which the paint types are repair types is smaller than a preset value, determining that the vehicle to be detected is a non-accident vehicle.

In the embodiment of the present disclosure, the satisfaction of the preset requirement of the positional relationship between the vehicle components means that the vehicle components are adjacent or close positional relationship, wherein the close positional relationship means that the distance between the two vehicle components is smaller than the preset range.

The preset values can be set according to actual conditions, and optionally, the preset values are 3. Specifically, if the positional relationship of the repair-type vehicle components meets the preset requirement and the number of the repair-type vehicle components is greater than or equal to 3, determining that the vehicle to be detected is an accident vehicle. For example: if the repair-type vehicle component includes an engine compartment cover, a left front fender, and a front bumper, the positional relationship of the repair-type vehicle component satisfies a preset requirement, and the number of repair-type vehicle components is 3. It is indicated that an excessive collision accident may occur in the left front of the vehicle, and thus, the vehicle to be detected is determined as an accident vehicle.

And if the position relation of the repair-type vehicle parts does not meet the preset requirement, determining that the vehicle to be detected is a non-accident vehicle. For example: the repair type vehicle parts are a left front fender and a right rear door, and the left front fender and the right rear door are unlikely to be damaged in a collision accident due to the fact that the two parts are far away from each other, and a plurality of scratch accidents can be caused, but no serious accident which causes damage to a vehicle structural member or a supporting member can be caused, so that the left front fender and the right rear door are judged to be damaged in two different slight scratch accidents, and therefore, the vehicle to be detected is determined to be a non-accident vehicle.

And if the number of the repair-type vehicle parts is smaller than a preset value, determining that the vehicle to be detected is a non-accident vehicle. For example: if the repair-type vehicle component includes a left front fender and a front bumper, the number of repair-type vehicle components is 2. It is indicated that a scratch crash may occur in the left front of the vehicle, and that only two vehicle components are damaged, it is indicated that the crash is not particularly severe, resulting in a low probability of damage to the structural components of the vehicle, and therefore the vehicle to be detected is determined as a non-accident vehicle.

The present disclosure relates to a vehicle collision accident determination method, comprising: for a vehicle part in a vehicle to be detected, acquiring paint film data of the vehicle part; determining whether the vehicle part is a repair part based on the paint film data; and determining the number of the repairing parts and the association relation between the repairing parts, and determining whether the vehicle to be detected is an accident vehicle or not based on the association relation between the number of the repairing parts and the repairing parts. In the embodiment of the disclosure, whether the vehicle part is a repair part is judged through paint film data of the vehicle part, the number of repair parts in the whole vehicle and the association relation among the repair parts are counted, whether the vehicle has a collision accident or not is further determined according to the number of the repair parts and the association relation among the repair parts, the accuracy of vehicle detection evaluation is improved, and the detection service timeliness is improved.

In one embodiment of the present disclosure, the method further comprises: if the vehicle to be detected is an accident vehicle, generating and displaying accident detection information, wherein the accident detection information is used for indicating a user to detect whether the vehicle to be detected is the accident vehicle or not; receiving a detection result input by a user; and if the detection result is an accident vehicle, marking the vehicle to be detected as the accident vehicle.

In an embodiment of the present disclosure, the accident detection information includes prompting a detection person that the vehicle may be an accident vehicle, and attention is paid to detecting whether the vehicle has an accident. The detection result input by the user may be an accident car or a non-accident car. If the detection result input by the user is an accident car, the car to be detected is marked as the accident car, and if the detection result input by the user is a non-accident car, the car to be detected is marked as the non-accident car.

Specifically, the user can adopt a manual detection mode to judge whether the structural parts of the vehicle are damaged, so as to determine whether the vehicle to be detected is an accident vehicle or not.

Further, the accident detection information may further include an accident occurrence location, for example: the accident occurrence part is the left front part, and prompts the detection personnel to detect whether the left front part of the vehicle has serious collision accident or not.

In the embodiment of the disclosure, after the accident vehicle is determined, the prompt information is generated and displayed to instruct the detection personnel to carry out recheck, so that the accuracy of vehicle detection is improved.

Fig. 3 is a schematic structural diagram of a vehicle detection device according to an embodiment of the disclosure, where the embodiment may be suitable for evaluating a vehicle condition of a second-hand vehicle, and the vehicle detection device may be implemented in a software and/or hardware manner.

As shown in fig. 3, the vehicle detection device provided in the embodiment of the present disclosure mainly includes: paint film data acquisition module 31, paint type determination module 32, quantity and relationship determination module 33 and accident vehicle determination module 34.

Wherein, the paint film data acquisition module 31 is used for aiming at the vehicle part in the vehicle to be detected and acquiring the paint film data of the vehicle part; a paint type determination module 32 for determining a paint type of the vehicle component based on the paint film data; a number and relationship determining module 33, configured to count the number of vehicle components of which the paint type is a repair type, and determine a positional relationship between the vehicle components of which the paint type is a repair type; an accident vehicle determination module 34 is configured to determine whether the vehicle to be detected is an accident vehicle based on the number and positional relationship of the vehicle components whose paint types are repair types.

In one embodiment of the present disclosure, paint type determination module 32 includes: a vehicle information acquisition unit configured to acquire vehicle information of the vehicle to be detected; the paint film standard range acquisition unit is used for acquiring a paint film standard range corresponding to the vehicle to be detected based on the vehicle information; a repair-type section determining unit configured to determine that a paint type of the vehicle section is a repair type if the paint film data is within the paint film standard range; an unrepaired-type section determining unit configured to determine that the paint type of the vehicle section is an unrepaired type if paint film data of the vehicle section is not within the paint film standard range.

In one embodiment of the present disclosure, the paint film data acquisition module 31 comprises: the system comprises a sampling paint film data acquisition unit, a detection unit and a detection unit, wherein the sampling paint film data acquisition unit is used for respectively carrying out paint film detection on a plurality of detection points in the vehicle part to obtain a plurality of sampling paint film data, and the plurality of detection points are positioned at different positions in the same vehicle part; and a paint film data determining unit configured to take an average value of the plurality of sampled paint film data as paint film data of the vehicle section.

In one embodiment of the present disclosure, paint type determination module 32 includes: a paint film data judging unit for judging whether paint film data of the vehicle part is within a paint film standard range; a maximum and minimum data determination unit configured to determine maximum sampled paint film data and minimum sampled paint film data from the plurality of sampled paint film data if the paint film data of the vehicle section is within a paint film standard range; the difference value calculating unit is used for calculating the difference value of the maximum sampling paint film data and the minimum sampling paint film data; and the repair part determining unit is further used for determining that the paint type of the vehicle part is repair if the difference value is larger than a preset value, and determining that the paint type of the vehicle part is unrepaired if the difference value is smaller than or equal to the preset value.

In one embodiment of the present disclosure, the accident car determination module 34 includes: an accident vehicle determining unit, configured to determine that the vehicle to be detected is an accident vehicle if the positional relationship between the vehicle components of which the paint type is a repair type meets a preset requirement, and the number of the vehicle components of which the paint type is a repair type is greater than or equal to a preset numerical value, where the satisfaction of the positional relationship between the vehicle components of which the distance between the vehicle components is within a preset range; and the non-accident vehicle determining unit is used for determining that the vehicle to be detected is a non-accident vehicle if the position relation between the vehicle parts of which the paint types are repair types does not meet the preset requirement or the number of the vehicle parts of which the paint types are repair types is smaller than a preset value.

In one embodiment of the present disclosure, the apparatus further comprises: the detection information display module is used for generating and displaying accident detection information if the vehicle to be detected is an accident vehicle, wherein the accident detection information is used for indicating a user to detect whether the vehicle to be detected is the accident vehicle or not; the detection result receiving module is used for receiving a detection result input by a user; and the accident car marking module is used for marking the car to be detected as the accident car if the detection result is the accident car.

The vehicle detection device provided in the embodiment of the present disclosure may perform the steps performed in the vehicle detection method provided in the embodiment of the present disclosure, and the performing steps and the beneficial effects are not described herein.

Fig. 4 is a schematic structural diagram of an electronic device in an embodiment of the disclosure. Referring now in particular to fig. 4, a schematic diagram of an electronic device 400 suitable for use in implementing embodiments of the present disclosure is shown. The electronic device 400 in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), wearable terminal devices, and the like, and fixed terminals such as digital TVs, desktop computers, smart home devices, and the like. The electronic device shown in fig. 4 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.

As shown in fig. 4, the electronic device 400 may include a processing means (e.g., a central processor, a graphics processor, etc.) 401 that may perform various suitable actions and processes to implement the vehicle detection method of the embodiments as described in the present disclosure according to a program stored in a Read Only Memory (ROM) 402 or a program loaded from a storage means 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the terminal apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.

In general, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, magnetic tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the terminal device 400 to communicate with other devices wirelessly or by wire to exchange data. While fig. 4 shows a terminal device 400 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flowchart, thereby implementing the vehicle detection method as described above. In such an embodiment, the computer program may be downloaded and installed from a network via communications device 409, or from storage 408, or from ROM 402. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 401.

It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.

The computer-readable medium carries one or more programs which, when executed by the terminal device, cause the terminal device to: for a vehicle part in a vehicle to be detected, acquiring paint film data of the vehicle part; determining a paint type of the vehicle part based on the paint film data; counting the number of the vehicle parts of which the paint types are repair types, and determining the position relation between the vehicle parts of which the paint types are repair types; and determining whether the vehicle to be detected is an accident vehicle or not based on the number of vehicle parts of which the paint type is repair type and the position relation.

Alternatively, the terminal device may perform other steps described in the above embodiments when the above one or more programs are executed by the terminal device.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

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

The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.

The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

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

According to one or more embodiments of the present disclosure, the present disclosure provides a vehicle detection method including: for a vehicle part in a vehicle to be detected, acquiring paint film data of the vehicle part; determining a paint type of the vehicle part based on the paint film data; counting the number of the vehicle parts of which the paint types are repair types, and determining the position relation between the vehicle parts of which the paint types are repair types; determining whether the vehicle to be detected is an accident vehicle based on the number of vehicle parts whose paint type is repair type and the positional relationship

According to one or more embodiments of the present disclosure, there is provided a vehicle detection apparatus including: the paint film data acquisition module is used for acquiring paint film data of a vehicle part aiming at the vehicle part in the vehicle to be detected; a paint type determination module for determining a paint type of the vehicle component based on the paint film data; the quantity and relation determining module is used for counting the quantity of the vehicle parts of which the paint types are repair types and determining the position relation among the vehicle parts of which the paint types are repair types; and the accident vehicle determining module is used for determining whether the vehicle to be detected is an accident vehicle or not based on the number and the position relation of the vehicle parts of which the paint types are repair types.

According to one or more embodiments of the present disclosure, the present disclosure provides an electronic device comprising:

one or more processors;

a memory for storing one or more programs;

The one or more programs, when executed by the one or more processors, cause the one or more processors to implement any of the vehicle detection methods as provided by the present disclosure.

According to one or more embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a vehicle detection method as any one of the present disclosure provides.

The disclosed embodiments also provide a computer program product comprising a computer program or instructions which, when executed by a processor, implements a vehicle detection method as described above.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).

Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.

Claims

1. A vehicle detection method, characterized by comprising:

determining a paint type of the vehicle part based on the paint film data;

2. The method of claim 1, wherein the determining a paint type of the vehicle component based on the paint film data comprises:

Acquiring vehicle information of the vehicle to be detected;

acquiring a paint film standard range corresponding to the vehicle to be detected based on the vehicle information;

If the paint film data is within the paint film standard range, determining that the paint type of the vehicle part is a repair type;

if the paint film data of the vehicle part is not within the paint film standard range, determining that the paint type of the vehicle part is an unrepaired type.

3. The method of claim 1, wherein the acquiring paint film data of the vehicle part comprises:

Respectively performing paint film detection on a plurality of detection points in the vehicle part to obtain a plurality of sampled paint film data, wherein the plurality of detection points are positioned at different positions in the same vehicle part;

the average value of the plurality of sampled paint film data is taken as the paint film data of the vehicle part.

4. The method of claim 3, wherein said determining a paint type of the vehicle component based on the paint film data comprises:

Judging whether paint film data of the vehicle part are in a paint film standard range or not;

Determining maximum sampled paint film data and minimum sampled paint film data from the plurality of sampled paint film data if the paint film data of the vehicle section is within the paint film standard range;

Calculating the difference value of the maximum sampled paint film data and the minimum sampled paint film data;

if the difference is greater than a preset value, determining that the paint type of the vehicle part is a repair type;

And if the difference value is smaller than or equal to a preset value, determining that the paint type of the vehicle part is an unrepaired type.

5. The method of claim 1, wherein determining whether the vehicle to be detected is an accident vehicle based on the number and positional relationship of the vehicle components for which the paint type is a repair type, comprises:

If the position relation between the vehicle components with the paint type being repair type meets the preset requirement and the number of the vehicle components with the paint type being repair type is larger than or equal to a preset value, determining that the vehicle to be detected is an accident vehicle, wherein the position relation between the vehicle components meets the preset requirement, namely that the distance between the vehicle components is within a preset range;

And if the position relation between the vehicle parts of which the paint types are repair types does not meet the preset requirement or the number of the vehicle parts of which the paint types are repair types is smaller than a preset value, determining that the vehicle to be detected is a non-accident vehicle.

6. The method according to claim 1, wherein the method further comprises:

If the vehicle to be detected is an accident vehicle, generating and displaying accident detection information, wherein the accident detection information is used for indicating a user to detect whether the vehicle to be detected is the accident vehicle or not;

Receiving a detection result input by a user;

and if the detection result is an accident vehicle, marking the vehicle to be detected as the accident vehicle.

7. A vehicle detection apparatus, characterized by comprising:

8. An electronic device, the electronic device comprising:

one or more processors;

a storage means for storing one or more programs;

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-6.

9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-6.

10. A computer program product comprising a computer program or instructions which, when executed by a processor, implements the method of any of claims 1-6.