Vehicle identification method and device, inspection system, storage medium, and electronic device

Abstract

The present disclosure discloses a vehicle identification method and device, a vehicle inspection system, a storage medium, and an electronic device. It is applied to the field of vehicle identification and vehicle safety inspection technology. The vehicle identification method includes: using a linear array camera to take pictures of the vehicle to be identified to obtain multiple linear array images of the vehicle to be identified; performing distortion correction on the multiple linear array images based on the movement speed of the vehicle to be identified to obtain multiple corrected images; combining the multiple corrected images to obtain a body image of the vehicle to be identified; identifying the model of the vehicle to be identified in the body image based on a first pattern recognition algorithm; and identifying the license plate number of the vehicle to be identified in the body image based on a second pattern recognition algorithm. The present disclosure can simultaneously identify the model and license plate number of the vehicle, and can also eliminate the influence of the movement speed of the vehicle to be identified on the image, reducing the deformation of each linear array image, thereby improving the accuracy of model and license plate number recognition.

Description

Background Art

[0002] After entering the 21st century, China's national economy has entered a period of rapid development. With the continuous improvement of people's living standards, the total number of cars has also begun to grow rapidly. People's requirements for vehicle management are getting higher and higher. The vehicle entrances and exits of parking lots, highway toll stations, government agencies, communities, etc. all need to identify vehicles according to specific needs.

[0003] For example, in the field of vehicle safety inspection, it is necessary to inspect container trucks and various trucks without stopping, and automatically identify the license plate number on the container truck or truck to achieve the binding of the license plate number and image, which can greatly improve the efficiency of safety inspection. In this case, the driver is required to drive through the radiation irradiation area, and since the energy and dose of the accelerator or radiation source are usually very high, it will cause great harm to the driver. Therefore, it is particularly important to accurately identify the vehicle model and license plate number, so as to control the radiation source according to the vehicle model to not irradiate the cab or irradiate at a low dose.

[0004] The commonly used method is to install a light curtain or area laser scanner in the detection channel to obtain the vehicle profile information and identify the type of vehicle based on the vehicle profile information. In addition, an area array camera is installed in the detection channel to capture the vehicle body image, and finally the vehicle license plate number is identified using image recognition methods.

[0005] In the above method, the image information obtained by the light curtain is small, the vehicle model and license plate number cannot be identified at the same time, and the installation is complicated; the cost of obtaining images through area laser scanners is high and easily affected by the environment; when obtaining vehicle body images through area array cameras, it is greatly affected by the vehicle speed and the recognition accuracy is low.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0007] The purpose of the present disclosure is to provide a vehicle identification method, a vehicle identification device, a vehicle inspection system using the vehicle identification device, a storage medium, and an electronic device, thereby overcoming one or more problems caused by the limitations and defects of the relevant technology at least to a certain extent.

[0008] According to one aspect of the present disclosure, there is provided a vehicle identification method, comprising:

[0009] Using a line array camera to take pictures of the vehicle to be identified to obtain multiple line array images of the vehicle to be identified;

[0010] Performing distortion correction on the plurality of line array images based on the movement speed of the vehicle to be identified to obtain a plurality of corrected images;

[0011] Combining the multiple corrected images to obtain a body image of the vehicle to be identified;

[0012] Identifying the type of the vehicle to be identified in the vehicle body image based on a first pattern recognition algorithm; and

[0013] The license plate number of the vehicle to be identified in the vehicle body image is identified based on a second pattern recognition algorithm.

[0014] In an exemplary embodiment of the present disclosure, the step of taking a picture of a vehicle to be identified by using a line array camera to acquire a plurality of line array images of the vehicle to be identified includes:

[0015] Provide a light emitting diode light source, wherein in a working mode, the light emitting diode light source is controlled to work in a manner of enhancing light brightness and combining high frequency flashing;

[0016] In the working mode of the light emitting diode light source, a line array camera is used to take pictures of the vehicle to be identified to obtain a plurality of line array images of the vehicle to be identified.

[0017] In an exemplary embodiment of the present disclosure, the LED light source is a LED light source with a narrow light emission angle.

[0018] In an exemplary embodiment of the present disclosure, the method further includes:

[0019] The brightness of the photographing environment is acquired in real time, and the exposure time of the line array camera is adjusted according to the brightness of the photographing environment.

[0020] In an exemplary embodiment of the present disclosure, the step of taking a picture of a vehicle to be identified by using a line array camera to acquire a plurality of line array images of the vehicle to be identified includes:

[0021] The linear array camera is controlled to continuously photograph the vehicle to be identified at a preset photographing frequency to obtain a plurality of linear array images of the vehicle to be identified.

[0022] In an exemplary embodiment of the present disclosure, the performing distortion correction on the plurality of line array images based on the movement speed of the vehicle to be identified to obtain a plurality of corrected images comprises:

[0023] Respectively acquiring the movement speed of the vehicle to be identified when each of the linear array images is captured;

[0024] Based on the movement speed of the vehicle to be identified when shooting each of the line array images and in combination with the movement speed determined by the preset shooting frequency, each of the line array images is horizontally compressed or horizontally stretched to obtain a plurality of corrected images.

[0025] In an exemplary embodiment of the present disclosure, controlling the line array camera to continuously photograph the vehicle to be identified at a preset photographing frequency to acquire a plurality of line array images of the vehicle to be identified includes:

[0026] Acquire the movement speed of the vehicle to be identified in real time, and adjust the preset photographing frequency according to the movement speed of the vehicle to be identified;

[0027] The linear array camera is controlled to continuously photograph the vehicle to be identified according to the adjusted preset photographing frequency to obtain a plurality of linear array images of the vehicle to be identified.

[0028] In an exemplary embodiment of the present disclosure, the first pattern recognition algorithm and the second pattern recognition algorithm are both one of a proximity algorithm, a Bayesian algorithm, a principal component analysis algorithm, a linear discriminant analysis algorithm, and a Gaussian mixture model.

[0029] In an exemplary embodiment of the present disclosure, the identifying the license plate number of the vehicle to be identified in the vehicle body image based on the second pattern recognition algorithm includes:

[0030] Determining whether the vehicle model to be identified is a preset vehicle model;

[0031] When it is determined that the vehicle model of the to-be-identified vehicle is the preset vehicle model, the license plate number of the to-be-identified vehicle in the vehicle body image is recognized based on a second pattern recognition algorithm.

[0032] In an exemplary embodiment of the present disclosure, the preset vehicle type is a truck.

[0033] According to one aspect of the present disclosure, there is provided a vehicle identification device, comprising:

[0034] An image acquisition module, used to take pictures of the vehicle to be identified using a line array camera to acquire multiple line array images of the vehicle to be identified;

[0035] A distortion correction module, used for performing distortion correction on the plurality of linear array images based on the movement speed of the vehicle to be identified to obtain a plurality of corrected images;

[0036] An image combination module, used for combining the multiple corrected images to obtain a body image of the vehicle to be identified;

[0037] A first recognition module, configured to recognize the type of the vehicle to be recognized in the vehicle body image based on a first pattern recognition algorithm; and

[0038] The second recognition module is used to recognize the license plate number of the vehicle to be recognized in the vehicle body image based on a second pattern recognition algorithm.

[0039] In an exemplary embodiment of the present disclosure, the image acquisition module includes:

[0040] A fill light unit, used to provide a light emitting diode light source, wherein, in the working mode, the light emitting diode light source is controlled to enhance the light brightness and work in a high-frequency flashing manner;

[0041] The image acquisition unit is used to use a line array camera to take pictures of the vehicle to be identified in the working mode of the light emitting diode light source to obtain multiple line array images of the vehicle to be identified.

[0042] In an exemplary embodiment of the present disclosure, the LED light source is a LED light source with a narrow light emission angle.

[0043] In an exemplary embodiment of the present disclosure, the device further includes:

[0044] The adjustment module is used to obtain the brightness of the photographing environment in real time and adjust the exposure time of the line array camera according to the brightness of the photographing environment.

[0045] In an exemplary embodiment of the present disclosure, the image acquisition module includes:

[0046] The image acquisition unit is used to control the line array camera to continuously take pictures of the vehicle to be identified at a preset shooting frequency to acquire a plurality of line array images of the vehicle to be identified.

[0047] In an exemplary embodiment of the present disclosure, the distortion correction module includes:

[0048] A speed acquisition unit, used to respectively acquire the moving speed of the vehicle to be identified when each of the linear array images is captured;

[0049] The distortion correction unit is used to horizontally compress or horizontally stretch each of the line array images based on the movement speed of the vehicle to be identified when shooting each of the line array images and the movement speed determined by the preset shooting frequency to obtain a plurality of corrected images.

[0050] In an exemplary embodiment of the present disclosure, the image acquisition unit includes:

[0051] A speed acquisition subunit, used for acquiring the moving speed of the vehicle to be identified in real time, and adjusting the preset photographing frequency according to the moving speed of the vehicle to be identified;

[0052] The image acquisition subunit is used to control the line array camera to continuously take pictures of the vehicle to be identified according to the adjusted preset shooting frequency to obtain multiple line array images of the vehicle to be identified.

[0053] In an exemplary embodiment of the present disclosure, the first pattern recognition algorithm and the second pattern recognition algorithm are both one of a proximity algorithm, a Bayesian algorithm, a principal component analysis algorithm, a linear discriminant analysis algorithm, and a Gaussian mixture model.

[0054] In an exemplary embodiment of the present disclosure, the second identification module includes:

[0055] A judging unit, used to judge whether the model of the vehicle to be identified is a preset model;

[0056] The identification unit is used to identify the vehicle number of the vehicle to be identified in the vehicle body image based on a second pattern recognition algorithm when it is determined that the vehicle type of the vehicle to be identified is the preset vehicle type.

[0057] In an exemplary embodiment of the present disclosure, the preset vehicle type is a truck.

[0058] According to one aspect of the present disclosure, a vehicle inspection system is provided, the system comprising any one of the vehicle identification devices described above.

[0059] According to one aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the vehicle identification method described in any one of the above is implemented.

[0060] According to one aspect of the present disclosure, there is provided an electronic device, including:

[0061] Processor; and

[0062] A memory, configured to store executable instructions of the processor;

[0063] Wherein, the processor is configured to execute any one of the vehicle identification methods described above by executing the executable instructions.

[0064] The present disclosure discloses a vehicle identification method, a vehicle identification device, a vehicle inspection system using the vehicle identification device, a storage medium, and an electronic device. The vehicle identification method uses a linear array camera to obtain multiple linear array images of a vehicle to be identified, and performs distortion correction on the multiple linear array images based on the speed of the vehicle to be identified to obtain multiple corrected images, and obtains a body image of the vehicle to be identified by combining the multiple corrected images. Finally, the model and license plate number of the vehicle to be identified in the body image are identified based on a first pattern recognition algorithm and a second pattern recognition algorithm. On the one hand, the multiple linear array images obtained by the linear array camera contain a large amount of information, so that the model and license plate number of the vehicle can be identified simultaneously based on the multiple linear array images; on the other hand, the multiple linear array images are corrected for distortion based on the speed of the vehicle to be identified, which reduces the deformation of each linear array image, thereby improving the accuracy of the recognition of the model and license plate number; on the other hand, due to the small size of the linear array camera, it is easy to install and debug, and the installation and debugging costs are reduced.

[0065] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The above and other features and advantages of the present disclosure will become more apparent by describing in detail its exemplary embodiments with reference to the accompanying drawings. Obviously, the drawings described below are only some embodiments of the present disclosure, and it is possible for a person of ordinary skill in the art to obtain other drawings based on these drawings without creative work. In the accompanying drawings:

[0067] Figure 1 A flow chart of a vehicle identification method disclosed herein;

[0068] Figure 2 A schematic diagram of a vehicle body image provided in an exemplary embodiment of the present disclosure;

[0069] Figure 3 A block diagram of a vehicle identification device disclosed herein;

[0070] Figure 4 A schematic diagram of determining a head of a vehicle to be identified provided in an exemplary embodiment of the present disclosure;

[0071] Figure 5 A schematic diagram of a module of an electronic device in an exemplary embodiment of the present disclosure is shown;

[0072] Figure 6 It is a schematic diagram of a program product in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0073] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0074] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, materials, devices, steps, etc. may be adopted. In other cases, known structures, methods, devices, implementations, materials or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0075] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or these functional entities or parts of functional entities may be implemented in one or more software hardened modules, or these functional entities may be implemented in different networks and / or processor devices and / or microcontroller devices.

[0076] In this exemplary embodiment, a vehicle identification method is first disclosed. Figure 1 As shown, the vehicle identification method may include:

[0077] Step S1, taking photos of the vehicle to be identified using a line array camera to obtain multiple line array images of the vehicle to be identified;

[0078] Step S2, performing distortion correction on the plurality of linear array images based on the movement speed of the vehicle to be identified to obtain a plurality of corrected images;

[0079] Step S3, combining the multiple corrected images to obtain a body image of the vehicle to be identified;

[0080] Step S4, identifying the type of the vehicle to be identified in the vehicle body image based on a first pattern recognition algorithm; and

[0081] Step S5: identifying the license plate number of the vehicle to be identified in the vehicle body image based on a second pattern recognition algorithm.

[0082] According to the vehicle identification method in this exemplary embodiment, on the one hand, the multiple line array images acquired by the line array camera contain a large amount of information, so that the vehicle model and license plate number can be simultaneously identified based on the multiple line array images; on the other hand, the multiple line array images are distorted and corrected based on the speed of the vehicle to be identified, thereby reducing the deformation of each line array image, thereby improving the accuracy of vehicle model and license plate number identification; on the other hand, due to the small size of the line array camera, it is easy to install and debug, thereby reducing the installation and debugging costs.

[0083] Next, each step in the vehicle identification method in this exemplary embodiment will be further described.

[0084] In step S1, a line array camera is used to take photos of a vehicle to be identified to obtain a plurality of line array images of the vehicle to be identified.

[0085] In this exemplary embodiment, the line array camera can be installed in the detection channel, and it can be set on the left or right side of the vehicle to be identified to obtain the body image of the left or right side of the vehicle to be identified, but the setting position of the line array camera in this exemplary embodiment is not limited to this. For example, according to specific needs, the line array camera can also be set above the vehicle to be identified to obtain a bird's-eye view of the body image of the vehicle to be identified. For another example, the line array camera can also be set at multiple positions of the vehicle to be identified to obtain the body images of the vehicle to be identified in multiple positions.

[0086] In this exemplary embodiment, the regional laser speed measurement and positioning module can be used to detect whether the vehicle to be identified enters the detection channel, and when it is detected that the vehicle to be identified enters the detection channel, the line array camera is started to continuously take pictures of the moving vehicle to be identified to obtain multiple line array images of the vehicle to be identified.

[0087] Furthermore, the use of a line array camera to take photos of the vehicle to be identified to obtain multiple line array images of the vehicle to be identified may include: controlling the line array camera to continuously take photos of the vehicle to be identified at a preset photo frequency to obtain multiple line array images of the vehicle to be identified. The preset photo frequency can be set by the developer according to the application scenario, and can also be calculated according to the speed of the vehicle to be identified when it enters the detection channel. This exemplary embodiment does not specifically limit this. It should be noted that the specific process of calculating the preset photo frequency according to the speed of the vehicle to be identified when it enters the detection channel is a well-known technology, so it will not be repeated here.

[0088] Furthermore, since the linear array image acquired through the preset photographing frequency will be distorted when the speed of the vehicle to be identified changes, in order to avoid distortion of the linear array image, controlling the linear array camera to continuously photograph the vehicle to be identified at a preset photographing frequency to acquire multiple linear array images of the vehicle to be identified may include: acquiring the movement speed of the vehicle to be identified in real time, and adjusting the preset photographing frequency according to the movement speed of the vehicle to be identified; controlling the linear array camera to continuously photograph the vehicle to be identified at the adjusted preset photographing frequency to acquire multiple linear array images of the vehicle to be identified.

[0089] On this basis, the distortion correction of the multiple line array images based on the movement speed of the vehicle to be identified to obtain multiple corrected images may include: based on the movement speed of the vehicle to be identified when shooting each line array image and in combination with the movement speed determined by the preset shooting frequency corresponding to each movement speed, each line array image is horizontally compressed or horizontally stretched to obtain multiple corrected images. It should be noted that the specific stretching and compression processing will be described in detail below, so it will not be repeated here.

[0090] Since the multiple line array images acquired by the line array camera contain a large amount of information, the vehicle model and license plate number can be simultaneously identified based on the multiple line array images; in addition, since the line array camera is small in size, it is easy to install and debug, reducing the installation and debugging costs.

[0091] In addition, when the ambient brightness is low, if the exposure time of the line scan camera is too short, the clarity of the image is low. When the ambient brightness is high, the exposure time of the line scan camera is long. Therefore, in order to obtain a line scan image with high clarity while reducing power consumption, the method may further include: obtaining the brightness of the photographing environment in real time, and adjusting the exposure time of the line scan camera according to the brightness of the photographing environment. In this exemplary embodiment, the brightness of the photographing environment is negatively correlated with the exposure time of the line scan camera. Specifically, when the brightness of the photographing environment is low, the exposure time of the line scan camera is increased, and when the brightness of the photographing environment is high, the exposure time of the line scan camera is shortened.

[0092] In addition, in order to obtain a clearer image, it is necessary to fill in the light of the object. At present, when a linear array camera is used to capture images of large objects, a laser light source is basically used to fill in the light of the object because the fill-in light distance is far. However, since the laser light source is a monochromatic light source, the image of the object obtained using the laser light source is a black and white image, and the use of a laser light source to fill in the light of the object will damage the human eye. Therefore, in order to obtain a color image without damaging the human eye, the use of a linear array camera to take pictures of the vehicle to be identified to obtain multiple linear array images of the vehicle to be identified may include:

[0093] A light emitting diode light source is provided, wherein, in a working mode, the light emitting diode light source is controlled to enhance the light emitting brightness and to work in a manner of combining high-frequency flashing; in the working mode of the light emitting diode light source, a linear array camera is used to take pictures of a vehicle to be identified to obtain multiple linear array images of the vehicle to be identified. In this example implementation, the high frequency refers to no less than 1000Hz, such as 2500Hz, 3000Hz, and so on. In one embodiment, the flashing frequency of the light emitting diode light source is 1KHz-10KHz. In this exemplary embodiment, since the light emitting diode light source is a white light source, a color image can be obtained after the light emitting diode light source is used to fill in the light of the object. By controlling the light emitting diode to enhance the light emitting brightness and to work in a manner of combining high-frequency flashing, the fill-in brightness requirement can be met, the power consumption can be reduced, and the human eye can be protected.

[0094] Furthermore, since the LED light source in this exemplary embodiment is used for vehicle identification and requires long-distance fill light, the LED light source adopts a narrow light-emitting angle LED light source. In this exemplary embodiment, the narrow light-emitting angle refers to a light-emitting angle less than 30 degrees; such as 15 degrees, 10 degrees, 7 degrees, etc. The narrow light-emitting angle LED light source can be a plurality of LEDs arranged in a straight line in the vertical direction, and the narrow light-emitting angle LED light source can also be a plurality of LEDs arranged in a spherical shape, and two reflective plates are arranged on the outside of the plurality of LEDs arranged in a spherical shape, and the angle between the two reflective plates is narrow, so that the light-emitting angle of the plurality of LEDs arranged in a spherical shape is narrower. In addition, it should be noted that the narrow light-emitting angle LED light source in this exemplary embodiment is not limited to this, and the narrow light-emitting angle LEDs realized in other ways also belong to the protection scope of the present disclosure.

[0095] In step S2, distortion correction is performed on the plurality of line array images based on the moving speed of the vehicle to be identified to obtain a plurality of corrected images.

[0096] In this exemplary embodiment, while the linear array camera continuously takes pictures of the vehicle to be identified, the vehicle speed measurement module obtains in real time the movement speed of the vehicle to be identified when each linear array image is taken; then, the corresponding linear array images are corrected according to the movement speed of the vehicle to be identified when each linear array image is taken to obtain multiple corrected images.

[0097] There are many ways for the vehicle speed measurement module to obtain the movement speed of the vehicle to be identified. For example, the movement speed of the vehicle to be identified can be directly measured by a speed sensor such as a speed measurement radar. For another example, the moment when the vehicle to be identified passes through two position sensors can be measured by two ground sensing coils and / or photoelectric switches and / or electronic light curtains, and the movement speed of the vehicle to be identified can be measured based on the distance between the two position sensors.

[0098] Since the frequency of shooting of the line array camera is very high, the difference in the movement speed of the vehicle to be identified when shooting multiple adjacent line array images is not large. Based on this, the movement speed of the vehicle to be identified can be obtained once every preset number of line array images. Take the preset number of line array images as 5 adjacent line array images as an example for explanation. First, the movement speed of the vehicle to be identified when shooting the first line array image is obtained. Next, the movement speed of the vehicle to be identified is obtained once every 5 adjacent line array images, that is, the movement speed of the vehicle to be identified when shooting the seventh line array image is obtained. Next, the movement speed of the vehicle to be identified when shooting the thirteenth line array image is obtained. Repeat the above steps until the photo of the vehicle to be identified is completed. It should be noted that the movement speeds corresponding to the first line array image to the sixth line array image are all the same, which are all the movement speeds of the vehicle to be identified when shooting the first line array image. The movement speeds corresponding to the seventh line array image to the twelfth line array image are all the same, which are all the movement speeds of the vehicle to be identified when shooting the seventh line array image. By analogy, the movement speeds corresponding to the subsequent line array images all conform to the above rules. The above method can reduce the workload of the vehicle speed measurement module and reduce the speed measurement cost without affecting the measurement accuracy.

[0099] In addition, when the linear array camera acquires multiple linear array images of the vehicle to be identified at a preset photographing frequency, the distortion correction of the multiple linear array images based on the movement speed of the vehicle to be identified to obtain multiple corrected images may include: respectively acquiring the movement speed of the vehicle to be identified when each of the linear array images is photographed; based on the movement speed of the vehicle to be identified when each of the linear array images is photographed and combined with the movement speed determined by the preset photographing frequency, horizontally compressing or horizontally stretching each of the linear array images to obtain multiple corrected images.

[0100] In this exemplary embodiment, since the specific method of obtaining the movement speed of the vehicle to be identified when shooting each line array image has been described in detail in step S2, it will not be repeated here. In the process of the linear array camera taking pictures of the vehicle to be identified, the area of ​​the vehicle to be identified contained in a line array image is different according to the different movement speeds of the vehicle to be identified. Specifically, when the speed of the vehicle to be identified is relatively fast, the area of ​​the vehicle to be identified contained in a line array image is relatively large, and when the speed of the vehicle to be identified is relatively slow, the area of ​​the vehicle to be identified contained in a line array image is relatively small. Since the size of each line array image is the same, if the area of ​​the vehicle to be identified contained in a line array image is relatively large, the line array image is visually compressed, and if the area of ​​the vehicle to be identified contained in a line array image is relatively small, the line array image is visually stretched.

[0101] On this basis, when the movement speed of the vehicle to be identified when the linear array image is captured is obtained, if the movement speed of the vehicle to be identified when the linear array image is captured is greater than the movement speed determined by the preset photographing frequency, the linear array image is horizontally stretched; if the movement speed of the vehicle to be identified when the linear array image is captured is less than the movement speed determined by the preset photographing frequency, the linear array image is horizontally compressed.

[0102] Through the above method, even if the movement speed of the vehicle to be identified keeps changing when acquiring multiple linear array images of the vehicle to be identified, the influence of the movement speed of the vehicle to be identified on the image can be eliminated by performing distortion correction on the multiple linear array images, thereby reducing the deformation of each linear array image and improving the accuracy of vehicle model and vehicle number recognition.

[0103] In step S3, the multiple corrected images are combined to obtain a body image of the vehicle to be identified.

[0104] In this exemplary embodiment, a plurality of corrected images may be combined based on the chronological order of the shooting of the corrected images to obtain a body image of the vehicle to be identified. Figure 2 The vehicle body image of the vehicle to be identified obtained through the above steps S1 to S3 is shown in Figure 2 It can be clearly identified that the vehicle to be identified is a container truck, as well as the character information on the container, wherein the character information on the container on the container truck includes: Beijing Foreign Transport Automobile Transportation Company and a contact number of 010-85367654.

[0105] It should be noted that in other exemplary embodiments of the present disclosure, multiple line array images can be combined based on the order of shooting time of each line array image to obtain a vehicle body image that has not been distortion corrected, and then the vehicle body image that has not been distortion corrected can be distortion corrected based on the movement speed of the vehicle to be identified when shooting each line array image to obtain a vehicle body image that has been distortion corrected.

[0106] In step S4, the type of the vehicle to be identified in the vehicle body image is identified based on a first pattern recognition algorithm.

[0107] In this exemplary embodiment, the first pattern recognition algorithm may be any one of a proximity algorithm, a Bayesian algorithm, a principal component analysis algorithm, a linear discriminant analysis algorithm, and a Gaussian mixture model, and the specific method may be determined according to the characteristics of the project. The vehicle model to be identified may include a van, a truck, a car, and a bus, and the exemplary embodiment does not specifically limit this. The following is an explanation of step S4 using the proximity algorithm as an example. The process of identifying the vehicle model to be identified in the vehicle body image based on the proximity algorithm may include: calculating the distance between the point corresponding to the vehicle to be identified and each point in the data set of known vehicle models; selecting k points with the smallest distance to the point corresponding to the vehicle to be identified, counting the probability of the sample vehicle of each model in the k points, and determining the model with the highest probability as the model of the vehicle to be identified. It should be noted that the above-mentioned known vehicle model data set includes data sets of various models such as car data sets, truck data sets, truck data sets, and bus data sets, so as to realize the identification of various vehicle models.

[0108] In step S5, the license plate number of the vehicle to be identified in the vehicle body image is identified based on a second pattern recognition algorithm.

[0109] In this exemplary embodiment, the second pattern recognition algorithm can be any one of a proximity algorithm, a Bayesian algorithm, a principal component analysis algorithm, a linear discriminant analysis algorithm, and a Gaussian mixture model, and the specific method can be determined according to the specific project. The vehicle number can be character information in the vehicle body image, wherein the character information includes at least one of text characters, numeric characters, and alphabetic characters. The following is an explanation of step S5 using the proximity algorithm as an example. The process of identifying the vehicle number of the vehicle to be identified in the vehicle body image based on the proximity algorithm may include: intercepting a character image to be identified of the vehicle to be identified in the vehicle body image, respectively calculating the distance between the point corresponding to the character image to be identified and each point in the data set of known characters; selecting k points with the smallest distance to the point corresponding to the character image to be identified, counting the probability of occurrence of sample characters in the same data set in the k points, and determining the character in the data set with the highest probability as the character corresponding to the character image to be identified, and repeating the above steps until the vehicle number of the vehicle to be identified in the vehicle body image is identified. It should be noted that the above-mentioned known character data set includes a numeric character data set, an alphabetic character data set, and a text character data set to achieve recognition of different characters.

[0110] In addition, the identifying the vehicle number of the vehicle to be identified in the vehicle body image based on the second pattern recognition algorithm may include: judging whether the vehicle model of the vehicle to be identified is a preset vehicle model; when it is judged that the vehicle model of the vehicle to be identified is the preset vehicle model, identifying the vehicle number of the vehicle to be identified in the vehicle body image based on the second pattern recognition algorithm.

[0111] In this exemplary embodiment, in the vehicle safety inspection, only the vehicle number recognition of the to-be-recognized vehicle such as a truck that can carry goods needs to be performed to bind the vehicle number of the to-be-recognized vehicle to the detection result, while the vehicle number recognition of the to-be-recognized vehicle such as a car that carries people needs not be performed. Therefore, when the vehicle type of the to-be-recognized vehicle in the vehicle body image is identified, the vehicle type judgment module can be used to judge whether the vehicle type of the to-be-recognized vehicle is a preset vehicle type, and when the vehicle type of the to-be-recognized vehicle is judged to be a preset vehicle type, the vehicle number of the to-be-recognized vehicle in the vehicle body image is recognized. The preset vehicle type can be a vehicle such as a truck that can carry goods, and the vehicle number can be character information on a vehicle such as a truck that can carry goods. Through the above-mentioned judgment mechanism, the to-be-recognized vehicle that needs to be recognized by the vehicle number recognition can be screened out, reducing the workload of the vehicle number recognition, thereby reducing the cost of the vehicle number recognition.

[0112] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0113] In an exemplary embodiment of the present disclosure, a vehicle identification device is also provided, such as Figure 3 As shown, the vehicle recognition device 100 may include: an image acquisition module 101, a distortion correction module 102, an image combination module 103, a first recognition module 104 and a second recognition module 105, wherein:

[0114] The image acquisition module 101 may be used to take a picture of the vehicle to be identified using a line array camera to acquire a plurality of line array images of the vehicle to be identified;

[0115] The distortion correction module 102 may be used to perform distortion correction on the plurality of linear array images based on the movement speed of the vehicle to be identified to obtain a plurality of corrected images;

[0116] The image combination module 103 may be used to combine the multiple corrected images to obtain a body image of the vehicle to be identified;

[0117] The first recognition module 104 may be used to recognize the type of the vehicle to be recognized in the vehicle body image based on a first pattern recognition algorithm; and

[0118] The second recognition module 105 may be configured to recognize the license plate number of the vehicle to be recognized in the vehicle body image based on a second pattern recognition algorithm.

[0119] In an exemplary embodiment of the present disclosure, the image acquisition module may include:

[0120] A fill light unit, used to provide a light emitting diode light source, wherein, in the working mode, the light emitting diode light source is controlled to enhance the light brightness and work in a high-frequency flashing manner;

[0121] The image acquisition unit is used to use a line array camera to take pictures of the vehicle to be identified in the working mode of the light emitting diode light source to obtain multiple line array images of the vehicle to be identified.

[0122] In an exemplary embodiment of the present disclosure, the LED light source may be a LED light source with a narrow light emitting angle.

[0123] In an exemplary embodiment of the present disclosure, the device may further include:

[0124] The adjustment module is used to obtain the brightness of the photographing environment in real time and adjust the exposure time of the line array camera according to the brightness of the photographing environment.

[0125] In an exemplary embodiment of the present disclosure, the image acquisition module may include:

[0126] The image acquisition unit is used to control the line array camera to continuously take pictures of the vehicle to be identified at a preset shooting frequency to acquire a plurality of line array images of the vehicle to be identified.

[0127] In an exemplary embodiment of the present disclosure, the distortion correction module may include:

[0128] A speed acquisition unit, used to respectively acquire the moving speed of the vehicle to be identified when each of the linear array images is captured;

[0129] The distortion correction unit is used to horizontally compress or horizontally stretch each of the line array images based on the movement speed of the vehicle to be identified when shooting each of the line array images and the movement speed determined by the preset shooting frequency to obtain a plurality of corrected images.

[0130] In an exemplary embodiment of the present disclosure, the image acquisition unit may include:

[0131] A speed acquisition subunit, used for acquiring the moving speed of the vehicle to be identified in real time, and adjusting the preset photographing frequency according to the moving speed of the vehicle to be identified;

[0132] The image acquisition subunit is used to control the line array camera to continuously take pictures of the vehicle to be identified according to the adjusted preset shooting frequency to obtain multiple line array images of the vehicle to be identified.

[0133] In an exemplary embodiment of the present disclosure, the first pattern recognition algorithm and the second pattern recognition algorithm are both one of a proximity algorithm, a Bayesian algorithm, a principal component analysis algorithm, a linear discriminant analysis algorithm, and a Gaussian mixture model.

[0134] In an exemplary embodiment of the present disclosure, the second identification module may include:

[0135] A judging unit, used to judge whether the model of the vehicle to be identified is a preset model;

[0136] The identification unit is used to identify the vehicle number of the vehicle to be identified in the vehicle body image based on a second pattern recognition algorithm when it is determined that the vehicle type of the vehicle to be identified is the preset vehicle type.

[0137] In an exemplary embodiment of the present disclosure, the preset vehicle type is a truck.

[0138] The specific details of each vehicle identification device module mentioned above have been described in detail in the corresponding vehicle identification method, so they will not be repeated here.

[0139] It should be noted that, although several modules or units of the device for execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0140] According to one aspect of the present disclosure, a vehicle inspection system is provided, which may include any one of the vehicle identification devices described above.

[0141] In this exemplary embodiment, for example, the above-mentioned vehicle inspection system may include a vehicle identification device, a control system, and a radiation imaging system. Among them, the vehicle identification device can be used to identify the model and license plate number of the vehicle to be identified. The control system can be used to determine the front position of the vehicle to be identified according to the model of the vehicle to be identified, and control the radiation source to emit radiation to illuminate the vehicle to be identified according to the front position. The radiation imaging device may include a radiation source for emitting radiation and a detector for detecting radiation that passes through the vehicle to be identified and / or scattered radiation, and for imaging the vehicle to be identified based on the radiation detected by the detector. As described above, when the vehicle to be identified enters the detection channel, first, the vehicle identification device identifies the model and license plate number of the vehicle, and then the control system can determine the front position of the vehicle to be identified (such as the front position of the vehicle to be identified) according to the model of the vehicle to be identified. Figure 4 As shown), the radiation source is controlled to emit radiation to illuminate the vehicle to be identified according to the position of the front of the vehicle. When the front of the vehicle to be identified enters the radiation scanning area, the control system controls the radiation source to scan at a low dose or not to scan. When the compartment of the vehicle to be identified enters the radiation scanning area, the control system controls the radiation source to scan at a higher dose. Finally, the radiation imaging system images the vehicle to be identified according to the radiation detected by the detector to obtain an inspection image of the compartment to be identified, and binds the license plate number of the vehicle to be identified with the inspection image, thereby completing the inspection of the vehicle to be identified.

[0142] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0143] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0144] Refer to the following Figure 5 The electronic device 600 according to this embodiment of the present invention is described. Figure 5 The electronic device 600 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0145] like Figure 5 As shown, the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), and a display unit 640.

[0146] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 The steps S1 shown in the figure are to take pictures of the vehicle to be identified by using a linear array camera to obtain multiple linear array images of the vehicle to be identified; step S2 is to perform distortion correction on the multiple linear array images based on the movement speed of the vehicle to be identified to obtain multiple corrected images; step S3 is to combine the multiple corrected images to obtain a body image of the vehicle to be identified; step S4 is to identify the model of the vehicle to be identified in the body image based on a first pattern recognition algorithm; and step S5 is to identify the license plate number of the vehicle to be identified in the body image based on a second pattern recognition algorithm.

[0147] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0148] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0149] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0150] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 660. As shown, the network adapter 660 communicates with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0151] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0152] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0153] refer to Figure 6 As shown, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0154] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0155] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0156] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0157] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0158] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0159] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0160] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A vehicle identification method, characterized in that: include: Provide a light emitting diode light source, wherein in a working mode, the light emitting diode light source is controlled to work in a manner of enhancing light brightness and combining high frequency flashing; In the working mode of the light emitting diode light source, a line array camera is used to take pictures of the vehicle to be identified to obtain a plurality of line array images of the vehicle to be identified; Respectively acquiring the movement speed of the vehicle to be identified when each of the linear array images is captured; Based on the movement speed of the vehicle to be identified when taking each of the line array images and in combination with the movement speed determined by a preset photographing frequency, each of the line array images is horizontally compressed or horizontally stretched to obtain a plurality of corrected images; Combining the multiple corrected images to obtain a body image of the vehicle to be identified; Identifying the type of the vehicle to be identified in the vehicle body image based on a first pattern recognition algorithm; Determining whether the vehicle to be identified is a preset vehicle model, wherein the preset vehicle model is a van or a truck; as well as Based on a second pattern recognition algorithm, the license plate number of the vehicle to be identified in the vehicle body image is identified, When it is determined that the vehicle type of the to-be-identified vehicle is the preset vehicle type, the front position of the to-be-identified vehicle is determined.

2. The vehicle identification method according to claim 1, characterized in that: The LED light source is a LED light source with a narrow light emitting angle.

3. The vehicle identification method according to claim 1, characterized in that: The method further comprises: The brightness of the photographing environment is acquired in real time, and the exposure time of the line array camera is adjusted according to the brightness of the photographing environment.

4. The vehicle identification method according to claim 1, characterized in that: The method of taking photos of the vehicle to be identified by using a line array camera to obtain a plurality of line array images of the vehicle to be identified comprises: The linear array camera is controlled to continuously photograph the vehicle to be identified at a preset photographing frequency to obtain a plurality of linear array images of the vehicle to be identified.

5. The vehicle identification method according to claim 1, characterized in that: The step of controlling the line array camera to continuously photograph the vehicle to be identified at a preset photographing frequency to obtain a plurality of line array images of the vehicle to be identified comprises: Acquire the movement speed of the vehicle to be identified in real time, and adjust the preset photographing frequency according to the movement speed of the vehicle to be identified; The linear array camera is controlled to continuously photograph the vehicle to be identified according to the adjusted preset photographing frequency to obtain a plurality of linear array images of the vehicle to be identified.

6. The vehicle identification method according to claim 1, characterized in that: The first pattern recognition algorithm and the second pattern recognition algorithm are both one of a proximity algorithm, a Bayesian algorithm, a principal component analysis algorithm, a linear discriminant analysis algorithm and a Gaussian mixture model.

7. A vehicle identification device, characterized in that: include: The image acquisition module comprises a fill light unit and an image acquisition unit, wherein the fill light unit is used to provide a light emitting diode light source, wherein, in a working mode, the light emitting diode light source is controlled to enhance the light brightness and work in a high-frequency flashing manner; the image acquisition unit is used to use a line array camera to take pictures of a vehicle to be identified in the working mode of the light emitting diode light source to obtain a plurality of line array images of the vehicle to be identified; The distortion correction module includes a speed acquisition unit and a distortion correction unit, wherein the speed acquisition unit is used to respectively acquire the movement speed of the vehicle to be identified when each of the line array images is photographed; and the distortion correction unit is used to respectively perform horizontal compression or horizontal stretching processing on each of the line array images based on the movement speed of the vehicle to be identified when each of the line array images is photographed and in combination with the movement speed determined by a preset photographing frequency to obtain a plurality of corrected images. An image combination module, used for combining the multiple corrected images to obtain a body image of the vehicle to be identified; A first recognition module, configured to recognize the type of the vehicle to be recognized in the vehicle body image based on a first pattern recognition algorithm; and A second recognition module, used for recognizing the license plate number of the vehicle to be recognized in the vehicle body image based on a second pattern recognition algorithm; The second identification module may include: A determination unit, used to determine whether the vehicle model to be identified is a preset vehicle model; the preset vehicle model is a van or a truck; an identification unit, configured to identify the vehicle number of the vehicle to be identified in the vehicle body image based on a second pattern recognition algorithm when the vehicle model to be identified is determined to be the preset vehicle model; The device is also used to determine the front position of the vehicle to be identified when it is determined that the vehicle type of the vehicle to be identified is the preset vehicle type.

8. The vehicle identification device according to claim 7, characterized in that: The LED light source is a LED light source with a narrow light emitting angle.

9. The vehicle identification device according to claim 7, characterized in that: The device also includes: The adjustment module is used to obtain the brightness of the photographing environment in real time and adjust the exposure time of the line array camera according to the brightness of the photographing environment.

10. The vehicle identification device according to claim 7, characterized in that: The image acquisition module comprises: The image acquisition unit is used to control the line array camera to continuously take pictures of the vehicle to be identified at a preset shooting frequency to acquire a plurality of line array images of the vehicle to be identified.

11. The vehicle identification device according to claim 10, characterized in that: The image acquisition unit comprises: A speed acquisition subunit, used for acquiring the moving speed of the vehicle to be identified in real time, and adjusting the preset photographing frequency according to the moving speed of the vehicle to be identified; The image acquisition subunit is used to control the line array camera to continuously take pictures of the vehicle to be identified according to the adjusted preset shooting frequency to obtain multiple line array images of the vehicle to be identified.

12. The vehicle identification device according to claim 7, characterized in that: The first pattern recognition algorithm and the second pattern recognition algorithm are both one of a proximity algorithm, a Bayesian algorithm, a principal component analysis algorithm, a linear discriminant analysis algorithm and a Gaussian mixture model.

13. A vehicle inspection system, characterized in that: The system comprises the vehicle identification device as described in any one of claims 7-12.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the vehicle identification method according to any one of claims 1 to 6 is implemented.

15. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to execute the vehicle identification method according to any one of claims 1 to 6 by executing the executable instructions.

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