WO2021138893A1 - Procédé et appareil de reconnaissance de plaque d'immatriculation de véhicule, dispositif électronique et support d'enregistrement - Google Patents

Procédé et appareil de reconnaissance de plaque d'immatriculation de véhicule, dispositif électronique et support d'enregistrement Download PDF

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WO2021138893A1
WO2021138893A1 PCT/CN2020/071330 CN2020071330W WO2021138893A1 WO 2021138893 A1 WO2021138893 A1 WO 2021138893A1 CN 2020071330 W CN2020071330 W CN 2020071330W WO 2021138893 A1 WO2021138893 A1 WO 2021138893A1
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Prior art keywords
license plate
recognition
plate recognition
target
images
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PCT/CN2020/071330
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English (en)
Chinese (zh)
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张恒瑞
宋翔
郭明坚
林雨辉
张劲松
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顺丰科技有限公司
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Priority to PCT/CN2020/071330 priority Critical patent/WO2021138893A1/fr
Priority to CN202080095192.2A priority patent/CN115298705A/zh
Publication of WO2021138893A1 publication Critical patent/WO2021138893A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition

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  • the invention relates to the technical field of license plate recognition, in particular to a license plate recognition method, device, electronic equipment and storage medium.
  • License plate recognition is a popular application field in the field of Optical Character Recognition (OCR).
  • OCR Optical Character Recognition
  • the license plate is about 1 meter away from the camera, coupled with the supporting lighting system and a proprietary camera, the image quality of the license plate image captured is very high, and naturally a high license plate recognition accuracy rate can be achieved.
  • the camera is sometimes far away from the vehicle or the license plate (for example, more than 3m), and the camera is a non-proprietary camera, such as the license plate recognition system applied to the loading and unloading port of the logistics transfer yard, used to identify loading and unloading vehicles License plate number.
  • the license plate recognition system uses a monitoring camera at the loading and unloading port instead of the proprietary camera in the aforementioned license plate recognition solution.
  • the monitoring camera of the loading and unloading port is generally installed close to the ceiling.
  • the distance between the camera and the license plate is greater than 3 meters, and the distance can be up to 10 meters or more during the parking and leaving of the vehicle, resulting in the license plate in the picture
  • the pixel area is too small, which brings challenges to recognition.
  • the complex lighting conditions in the transit field such as insufficient lighting, backlighting, and license plate reflections, also pose challenges for recognition.
  • the vehicles in the transit yard are mainly large trucks, and only the rear license plate can be seen when the truck is loading and unloading.
  • license plate recognition methods include license plate detection, image binarization, character segmentation, character recognition and other steps. Because license plate detection will have misdetection, it is generally necessary to add an image quality judgment link before image binarization.
  • the link uses a small classification network to eliminate non-license plate and fuzzy license plate images.
  • the traditional method contains so many links, which leads to a lot of manpower for data labeling and model training in each link.
  • the final recognition accuracy is obtained by multiplying the accuracy of each link. Because there are too many links, and each link cannot reach 100%, there is a bottleneck that is difficult to break through the accuracy of license plate recognition.
  • the embodiments of the present application provide a license plate recognition method, device, electronic equipment, and storage medium, which can effectively eliminate the influence of misdetection of license plates and blurring of license plate motion on the result of license plate recognition, and greatly improve the accuracy of license plate recognition.
  • the present application provides a method for recognizing a license plate, and the method for recognizing a license plate includes:
  • performing license plate recognition on the multiple license plate images to obtain multiple license plate recognition results includes:
  • the license plate recognition result of the target license plate image is determined.
  • the extracting features of the target license plate image to obtain a feature map corresponding to the license plate image includes:
  • the target license plate image is input into a preset convolutional neural network model to extract features corresponding to the license plate image using the convolutional neural network model, and output a feature map corresponding to the target license plate image.
  • the calculating the tensor of the license plate character recognition result according to the attention map includes:
  • the weighted graph is input into a preset long-term short-term memory network model, and the license plate character recognition result tensor corresponding to the target license plate image is output.
  • the license plate character recognition tensor includes a character recognition tensor at multiple positions, and the character recognition tensor at each position includes a plurality of candidate characters. According to the license plate character recognition tensor, it is determined
  • the license plate recognition result of the target license plate image includes:
  • the candidate character with the highest confidence in each position is taken as the character recognition result of the position, and the license plate recognition result of the target license plate image is obtained.
  • the fusing the multiple license plate recognition results to determine the license plate recognition result of the target vehicle includes:
  • the fusion of the multiple license plate recognition results to obtain the fused license plate recognition result includes:
  • the acquiring multiple license plate images of the target vehicle includes:
  • the license plate area in the multiple images is cropped and normalized to a preset size to obtain multiple license plate images of the target vehicle.
  • the detecting the license plate in the surveillance video to obtain multiple images including the license plate includes:
  • a multi-target detection method is used to detect the license plate in the surveillance video to obtain multiple images including the license plate.
  • the present application provides a license plate recognition device, the license plate recognition device includes:
  • the acquiring unit is used to acquire multiple license plate images of the target vehicle
  • a recognition unit configured to perform license plate recognition on the multiple license plate images to obtain multiple license plate recognition results
  • the determining unit is used for fusing the multiple license plate recognition results to determine the license plate recognition result of the target vehicle.
  • the identification unit is specifically configured to:
  • the license plate recognition result of the target license plate image is determined.
  • the identification unit is specifically configured to:
  • the target license plate image is input into a preset convolutional neural network model to extract features corresponding to the license plate image using the convolutional neural network model, and output a feature map corresponding to the target license plate image.
  • the identification unit is specifically configured to:
  • the weighted graph is input into a preset long-term short-term memory network model, and the license plate character recognition result tensor corresponding to the target license plate image is output.
  • the license plate character recognition tensor includes character recognition tensors at multiple positions, and the character recognition tensor at each position includes multiple candidate characters, and the recognition unit is specifically configured to:
  • the candidate character with the highest confidence in each position is taken as the character recognition result of the position, and the license plate recognition result of the target license plate image is obtained.
  • the determining unit is specifically configured to:
  • the determining unit is specifically configured to:
  • the acquiring unit is specifically configured to:
  • the license plate area in the multiple images is cropped and normalized to a preset size to obtain multiple license plate images of the target vehicle.
  • the acquiring unit is specifically configured to:
  • a multi-target detection method is used to detect the license plate in the surveillance video to obtain multiple images including the license plate.
  • this application also provides an electronic device, which includes:
  • One or more processors are One or more processors;
  • One or more application programs wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the steps in the license plate recognition method described above.
  • the present application also provides a computer-readable storage medium on which a computer program is stored, and the computer program is loaded by a processor to execute the steps in the license plate recognition method.
  • multiple license plate images of the target vehicle are acquired; license plate recognition is performed on the multiple license plate images to obtain multiple license plate recognition results; multiple license plate recognition results are merged to determine the license plate recognition result of the target vehicle.
  • multiple license plate recognition results are obtained, and the multiple license plate recognition results are merged to determine the license plate recognition result of the target vehicle, because the final license plate recognition result of the target vehicle is It is determined by the fusion of multiple license plate recognition results, so it can effectively eliminate the influence of license plate misdetection and license plate motion blur on the license plate recognition result, and greatly improve the accuracy of license plate recognition.
  • FIG. 1 is a schematic diagram of a scene of a license plate recognition system provided by an embodiment of the present invention
  • FIG. 2 is a schematic flowchart of an embodiment of a method for recognizing a license plate provided by an embodiment of the present invention
  • FIG. 3 is a schematic flowchart of an embodiment in step 202 in an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of a specific scene of a license plate recognition scene in an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of the attention map and the original image of the license plate in the embodiment of the present invention after being superimposed;
  • Fig. 6 is a schematic structural diagram of an embodiment of a license plate recognition device in an embodiment of the present invention.
  • Fig. 7 is a schematic structural diagram of an embodiment of an electronic device in an embodiment of the present invention.
  • first and second are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with “first” and “second” may explicitly or implicitly include one or more of the features. In the description of the present invention, “plurality” means two or more than two, unless otherwise specifically defined.
  • the embodiments of the present invention provide a license plate recognition method, device, electronic equipment, and storage medium, which will be described in detail below.
  • FIG. 1 is a schematic diagram of a scene of a license plate recognition system provided by an embodiment of the present invention.
  • the license plate recognition system may include a monitoring device 100 and an electronic device 200.
  • the monitoring device 100 and the electronic device 20 are connected to each other through a network.
  • a license plate recognition device such as the electronic device in FIG. 1, the monitoring device 100 can access the electronic device 200.
  • the electronic device 200 is mainly used to obtain multiple license plate images of a target vehicle; perform license plate recognition on the multiple license plate images respectively to obtain multiple license plate recognition results; merge the multiple license plate recognition results, Determine the license plate recognition result of the target vehicle.
  • the monitoring device 100 is mainly used to shoot monitoring video images and transmit them to the electronic device 200.
  • the electronic device 200 may be an independent server, or a server network or server cluster composed of servers.
  • the electronic device 200 described in the embodiment of the present invention includes, but is not limited to, a computer and a network.
  • the cloud server is composed of a large number of computers or network servers based on Cloud Computing.
  • the electronic device 200 and the monitoring device 100 can communicate through any communication method, including but not limited to, based on the 3rd Generation Partnership Project (3rd Generation Partnership Project, 3GPP), Long Term Evolution (Long Term) Evolution, LTE), Worldwide Interoperability for Microwave Access (WiMAX) mobile communications, or based on the TCP/IP protocol suite (TCP/IP Protocol Suite, TCP/IP), User Datagram Protocol (User Datagram Protocol, UDP) protocol computer network communication, etc.
  • 3rd Generation Partnership Project 3rd Generation Partnership Project, 3GPP
  • Long Term Evolution Long Term Evolution
  • LTE Long Term Evolution
  • WiMAX Worldwide Interoperability for Microwave Access
  • TCP/IP protocol suite TCP/IP Protocol Suite, TCP/IP
  • User Datagram Protocol User Datagram Protocol
  • UDP User Datagram Protocol
  • the monitoring device 100 used in the embodiment of the present invention may be a device including both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing two-way communication on a two-way communication link.
  • a monitoring device 100 may include a cellular or other communication device with a single-line display or a multi-line display or a cellular or other communication device without a multi-line display.
  • the specific monitoring device 100 may specifically be a monitoring camera.
  • Fig. 1 is only an application scenario with the solution of this application, and does not constitute a limitation on the application scenario of the solution of this application.
  • Other application environments may also include those shown in Fig. 1 Show more or fewer electronic devices, or the network connection relationship of electronic devices.
  • Fig. 1 Show more or fewer electronic devices, or the network connection relationship of electronic devices.
  • only one electronic device and two monitoring devices are shown in Figure 1.
  • the license plate recognition system may also include one or more other The electronic device, or/and one or more other monitoring devices connected to the electronic device network, are not specifically limited here.
  • the license plate recognition system may also include a memory 300 for storing data, such as storing video data, for example, video files collected by a monitoring device.
  • FIG. 1 the scene schematic diagram of the license plate recognition system shown in FIG. 1 is only an example.
  • the license plate recognition system and the scene described in the embodiment of the present invention are intended to explain the technical solutions of the embodiments of the present invention more clearly, and do not constitute As for the limitation of the technical solutions provided by the embodiments of the present invention, those of ordinary skill in the art know that with the evolution of the license plate recognition system and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present invention are equally applicable to similar technical problems.
  • an embodiment of the present invention provides a license plate recognition method.
  • the execution body of the license plate recognition method is a license plate recognition device, and the license plate recognition device is applied to electronic equipment.
  • the license plate recognition method includes: acquiring multiple license plate images of a target vehicle; Performing license plate recognition on the multiple license plate images respectively to obtain multiple license plate recognition results; fusing the multiple license plate recognition results to determine the license plate recognition result of the target vehicle.
  • the license plate recognition method includes:
  • the acquiring multiple license plate images of the target vehicle may include: acquiring the collected surveillance video of the target vehicle; detecting the license plate in the surveillance video to obtain the license plate The multiple images of the multiple images; the license plate area in the multiple images is cropped and normalized to a preset size to obtain multiple license plate images of the target vehicle.
  • the acquired monitoring video of the target vehicle may be a monitoring video shot by one or more monitoring devices.
  • the detecting the license plate in the surveillance video to obtain multiple images including the license plate includes: detecting the license plate in the surveillance video by using a multi-target detection method to obtain multiple images including the license plate.
  • the SSD Single Shot MultiBox Detector
  • the multi-target detection network model can be used to continuously detect the license plate, and the detected license plate image is cropped from the original image and normalized to a uniform size. As input to the image sequence of the subsequent steps.
  • the SSD algorithm is a multi-target detection algorithm that directly predicts the target category and bounding box.
  • the SSD algorithm uses feature maps of different convolutional layers for synthesis to achieve the same effect.
  • the main network structure of the SSD algorithm is VGG16.
  • the loading and unloading port of the logistics transfer yard it will also be used to identify the license plate number of the loading and unloading vehicle.
  • the transit site generally uses surveillance cameras at the loading and unloading ports instead of proprietary cameras.
  • the monitoring camera of the loading and unloading port is generally installed close to the ceiling.
  • the distance between the camera and the license plate is greater than 3 meters, and the distance can be up to 10 meters or more during the parking and leaving of the vehicle, resulting in the license plate in the picture
  • the pixel area is too small, which brings challenges to recognition.
  • the complex lighting conditions in the transit field such as insufficient lighting, backlighting, and license plate reflections, also pose challenges for recognition.
  • the vehicles in the transit yard are mainly large trucks, and only the rear license plate can be seen when the truck is loading and unloading.
  • the first line of Chinese characters and letters of the double-layer yellow license plate at the rear of the vehicle are too small, which makes it difficult to recognize, especially Chinese characters with many strokes, such as "Gan”, “Zang", and "E” are stuck together, making it more difficult to recognize.
  • the upper part of the license plate of some models will be blocked by the rear bumper, and the characters in the first line cannot be recognized.
  • the main application scenario in the embodiment of the present invention can be the license plate number recognition scenario of the loading and unloading vehicle at the loading and unloading port.
  • the target vehicle is the loading and unloading vehicle at the loading and unloading port
  • the monitoring device is the monitoring device set at the loading and unloading port of the transfer yard.
  • license plate recognition can be performed on the multiple license plate images to obtain multiple license plate recognition results. Specifically, the license plate recognition is performed on each license plate image to obtain a license plate recognition result.
  • performing license plate recognition on the multiple license plate images to obtain multiple license plate recognition results may further include:
  • license plate images in the plurality of license plate images as target license plate images extract features of the target license plate images to obtain feature maps corresponding to the license plate images.
  • extracting features of the target license plate image to obtain a feature map corresponding to the license plate image includes: inputting the target license plate image into a preset convolutional neural network (Convolutional Neural Networks, CNN) model to use the The convolutional neural network model extracts features corresponding to the license plate image, and outputs a feature map corresponding to the target license plate image.
  • CNN convolutional Neural Networks
  • the preset CNN model may be obtained after training using a large amount of collected license plate image data.
  • the attention map is the visual attention mask of the characters in the feature map.
  • the following formula (1), formula (2), and formula (3) can be used to extract the attention map of the characters in the feature map. :
  • V ⁇ is a preset vector
  • w s , w f1 , w f2 , and w f3 are the parameters of the weight matrix of the CNN model obtained in advance before the calculation, which will be updated during the CNN model training process
  • st is the preset
  • the attention map (also called attention mask) ⁇ t is obtained after normalization by the softmax function, ⁇ t is the attention map calculated at time t, which is a matrix, ⁇ t,i,j are the elements of the matrix ⁇ t, i , J is the row number and column number of the matrix respectively, which is a scalar.
  • Long Short-Term Memory (LSTM, Long Short-Term Memory) is a time cyclic neural network, which is specially designed to solve the long-term dependence problem of general RNN (recurrent neural network). All RNNs have a Repeat the chain form of neural network modules. In a standard RNN, this repeated structural module has only a very simple structure, such as a tanh layer.
  • the calculation of the license plate character recognition result tensor according to the attention map includes: weighting the attention map and the feature map to obtain a weighted map; and inputting the weighted map to a preset length
  • the short-term memory network model outputs the license plate character recognition result tensor corresponding to the target license plate image.
  • Steps 302 and 303 are the same LSTM.
  • these input values and output values are all vectors.
  • c t-1 is the one-hot encoding vector of the previous character number in the target license plate image
  • u t,c is the feature vector of the weighted image obtained after the weighted summation of the attention map and the feature map
  • w c and w u1 are the parameters of the weight matrix of the LSTM model obtained in advance, and are updated during the training of the LSTM model.
  • st is the hidden state of the LSTM at time t, which is a column vector
  • o t is the output vector of the LSTM at time t.
  • the character recognition result tensor at time t is obtained by the following formula (7):
  • the license plate character recognition tensor includes multiple positions of the character recognition tensor, and the character recognition tensor at each position includes multiple candidate characters.
  • the target license plate is determined according to the license plate character recognition tensor.
  • the image's license plate recognition result can include:
  • fusing the multiple license plate recognition results to determine the license plate recognition result of the target vehicle may include:
  • the fusion of the plurality of license plate recognition results to obtain the fused license plate recognition result may further include: calculating the number of qualified characters whose character recognition results meet the preset reliability requirements in each license plate recognition result; taking qualified characters For the most N number of license plate recognition results, the license plate recognition results are fused, and the fused license plate recognition result is obtained.
  • the rules are as follows: a) Clear and correctly recognized characters, the confidence of Top5 candidate characters, the confidence of Top1 character is much greater than the confidence of other candidate characters, for example, the confidence of Top1 is greater than 0.9, and The total confidence of the remaining candidate characters is 0.1; b) When the characters are blurred, the confidence of the Top5 candidate characters cannot be widened. For example, the confidence of Top1 is 0.6 and the confidence of Top2 is 0.3.
  • a confidence threshold can be set, the number of qualified characters in the license plate recognition result that meets the confidence threshold can be calculated, and the number of qualified characters in the license plate recognition result can be used as an index for the quality of the license plate. For example, if the confidence threshold is 0.9, a certain license plate recognition result includes 6 characters, which are distributed in order of 0.95, 0.98, 0.97, 0.95, 0.89, 0.94. Among them, the number of characters that meet the confidence threshold is 5, that is, qualified characters The number is 5.
  • the N license plate recognition results with the largest number of qualified characters are taken, and the license plate recognition results are merged, and the following methods can be specifically adopted to obtain the merged license plate recognition result:
  • each license plate recognition result contains the position information of each character, based on this position information, character alignment is performed, and then the characters in each position are voted and sorted to obtain the best character at that position.
  • the specific steps are as follows:
  • the abscissa of the character vertex x and the character width width can be obtained according to the following formula, and the abscissa of the character center point can be calculated:
  • the candidate character with the highest score is subsequently obtained as the optimal character at that position, and the optimal characters in all positions are arranged in the order from left to right, and the result of the fusion license plate recognition can be obtained.
  • the multiple license plate recognition results are merged, and after the merged license plate recognition result is obtained, the merged license plate recognition result may be a complete license plate information, or it may only include a part of the license plate information.
  • the license plate recognition result of the target vehicle can be determined by searching in a preset license plate database according to the fusion license plate recognition result. For example, take the license plate recognition scene at the loading and unloading port of the transfer yard as an example, according to the result of the license plate recognition after fusion, it can be matched in the vehicle database of the logistics transfer yard to obtain the complete license plate number.
  • multiple license plate images of the target vehicle are acquired; license plate recognition is performed on the multiple license plate images to obtain multiple license plate recognition results; multiple license plate recognition results are merged to determine the license plate recognition result of the target vehicle.
  • multiple license plate recognition results are obtained by recognizing multiple license plate images of the target vehicle, and the multiple license plate recognition results are merged to determine the license plate recognition result of the target vehicle. Because the final license plate recognition of the target vehicle is determined The result is determined by the fusion of multiple license plate recognition results, so it can effectively eliminate the influence of license plate misdetection and license plate motion blur on the license plate recognition result, and greatly improve the accuracy of license plate recognition.
  • license plate recognition methods include license plate detection, image binarization, character segmentation, character recognition and other steps. Because license plate detection will have misdetection, it is generally necessary to add an image quality judgment link before image binarization. Using a small classification network to eliminate non-license plate and fuzzy license plate images, the traditional method contains so many links, which leads to a lot of manpower for data labeling and model training in each link. In addition, the final recognition accuracy rate is obtained by multiplying the accuracy rates of each link. Because there are too many links, and each link cannot reach 100%, there is a bottleneck that is difficult to break through in the recognition accuracy rate.
  • the image quality judgment, image binarization, character segmentation and character recognition in the existing license plate recognition process are integrated into a deep neural network (Attention-OCR).
  • Attention-OCR deep neural network
  • the Attention-OCR network structure is shown in Figure 1, including the following parts: a CNN network model and an LSTM network model.
  • a CNN network model By inputting an image sequence in CNN, outputting the feature map corresponding to the image sequence, extracting the attention map from each feature map , Input the attention map into the LSTM to get the character recognition result tensor at time t, analyze the character recognition result tensor at time t to get the confidence of each candidate character, and take the character with the highest confidence as the character recognition result at that position.
  • the attention map After visualizing the attention map, it is superimposed on the original license plate image, as shown in Figure 5, where the small square is the attention map.
  • the number of qualified characters in the license plate recognition result that meets the confidence threshold is calculated, the number of qualified characters in the license plate recognition result is used as the index of license plate quality judgment, and the five license plate recognition results with the largest number of qualified characters are taken to merge the license plate recognition results. According to the result of the license plate recognition after fusion, search in the preset license plate database to determine the license plate recognition result of the target vehicle.
  • an embodiment of the present invention also provides a license plate recognition device.
  • the license plate recognition device 600 includes:
  • the acquiring unit 601 is configured to acquire multiple license plate images of the target vehicle
  • the recognition unit 602 is configured to perform license plate recognition on the multiple license plate images to obtain multiple license plate recognition results;
  • the determining unit 603 is configured to merge the multiple license plate recognition results to determine the license plate recognition result of the target vehicle.
  • the identification unit 602 is specifically configured to:
  • the license plate recognition result of the target license plate image is determined.
  • the identification unit 602 is specifically configured to:
  • the target license plate image is input into a preset convolutional neural network model to extract features corresponding to the license plate image using the convolutional neural network model, and output a feature map corresponding to the target license plate image.
  • the identification unit 602 is specifically configured to:
  • the weighted graph is input into a preset long-term short-term memory network model, and the license plate character recognition result tensor corresponding to the target license plate image is output.
  • the license plate character recognition tensor includes character recognition tensors at multiple positions, and the character recognition tensor at each position includes multiple candidate characters, and the recognition unit 602 is specifically configured to:
  • the candidate character with the highest confidence in each position is taken as the character recognition result of the position, and the license plate recognition result of the target license plate image is obtained.
  • the determining unit 603 is specifically configured to:
  • the determining unit 603 is specifically configured to:
  • the acquiring unit 601 is specifically configured to:
  • the license plate area in the multiple images is cropped and normalized to a preset size to obtain multiple license plate images of the target vehicle.
  • the acquiring unit 601 is specifically configured to:
  • a multi-target detection method is used to detect the license plate in the surveillance video to obtain multiple images including the license plate.
  • multiple license plate images of the target vehicle are acquired by the acquiring unit 601; the recognition unit 602 performs license plate recognition on the multiple license plate images to obtain multiple license plate recognition results; the determining unit 603 merges the multiple license plate recognition results, Determine the result of the license plate recognition of the target vehicle.
  • multiple license plate recognition results are obtained by recognizing multiple license plate images of the target vehicle, and the multiple license plate recognition results are merged to determine the license plate recognition result of the target vehicle. Because the final license plate recognition of the target vehicle is determined The result is determined by the fusion of multiple license plate recognition results, so it can effectively eliminate the influence of license plate misdetection and license plate motion blur on the license plate recognition result, and greatly improve the accuracy of license plate recognition.
  • An embodiment of the present invention also provides an electronic device that integrates any of the license plate recognition devices provided in the embodiments of the present invention, and the electronic device includes:
  • One or more processors are One or more processors;
  • the embodiment of the present invention also provides an electronic device that integrates any of the license plate recognition methods provided in the embodiment of the present invention.
  • FIG. 7 shows a schematic structural diagram of an electronic device involved in an embodiment of the present invention, specifically:
  • the electronic device may include one or more processing core processors 701, one or more computer-readable storage medium memory 702, power supply 703, input unit 704 and other components.
  • processing core processors 701 one or more computer-readable storage medium memory 702, power supply 703, input unit 704 and other components.
  • FIG. 7 does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or a combination of certain components, or different component arrangements. among them:
  • the processor 701 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It runs or executes software programs and/or modules stored in the memory 702, and calls Data, perform various functions of electronic equipment and process data, so as to monitor the electronic equipment as a whole.
  • the processor 701 may include one or more processing cores; preferably, the processor 701 may integrate an application processor and a modem processor, where the application processor mainly processes the operating system, user interface, application programs, etc. , The modem processor mainly deals with wireless communication. It can be understood that the foregoing modem processor may not be integrated into the processor 701.
  • the memory 702 may be used to store software programs and modules.
  • the processor 701 executes various functional applications and data processing by running the software programs and modules stored in the memory 702.
  • the memory 702 may mainly include a program storage area and a data storage area.
  • the program storage area may store an operating system, an application program required by at least one function (such as a sound playback function, an image playback function, etc.), etc.; Data created by the use of electronic equipment, etc.
  • the memory 702 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.
  • the memory 702 may further include a memory controller to provide the processor 701 with access to the memory 702.
  • the electronic device also includes a power supply 703 for supplying power to various components.
  • the power supply 703 can be logically connected to the processor 701 through a power management system, so that functions such as charging, discharging, and power consumption management can be managed through the power management system.
  • the power supply 703 may also include any components such as one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, and a power status indicator.
  • the electronic device may further include an input unit 704, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.
  • an input unit 704 can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.
  • the electronic device may also include a display unit, etc., which will not be repeated here.
  • the processor 701 in the electronic device will load the executable file corresponding to the process of one or more application programs into the memory 702 according to the following instructions, and the processor 701 will run and store the executable file in the memory 702.
  • the application programs in the memory 702, thereby realizing various functions, are as follows:
  • an embodiment of the present invention provides a computer-readable storage medium, which may include: read only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks, etc. .
  • a computer program is stored thereon, and the computer program is loaded by the processor to execute the steps in any of the license plate recognition methods provided in the embodiments of the present invention.
  • the computer program can be loaded by the processor to perform the following steps:
  • each of the above units or structures can be implemented as independent entities, or can be combined arbitrarily, and implemented as the same or several entities.
  • specific implementation of each of the above units or structures please refer to the previous method embodiments. No longer.

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Abstract

Procédé et appareil de reconnaissance de plaque d'immatriculation de véhicule, dispositif électronique et support d'enregistrement. Le procédé de reconnaissance de plaque d'immatriculation de véhicule comprend : l'acquisition d'une pluralité d'images de plaque d'immatriculation de véhicule d'un véhicule cible (201); la réalisation d'une reconnaissance de plaque d'immatriculation de véhicule sur la pluralité d'images de plaque d'immatriculation de véhicule respectivement, de façon à obtenir une pluralité de résultats de reconnaissance de plaque d'immatriculation de véhicule (202); et la fusion de la pluralité de résultats de reconnaissance de plaque d'immatriculation de véhicule, et la détermination d'un résultat de reconnaissance de plaque d'immatriculation de véhicule du véhicule cible (203). Lorsque le résultat de reconnaissance finalement déterminé de la plaque d'immatriculation de véhicule du véhicule cible est déterminé par fusion d'une pluralité de résultats de reconnaissance de la plaque d'immatriculation de véhicule, l'influence sur le résultat de reconnaissance de la plaque d'immatriculation de véhicule provoquée par une détection erronée de la plaque d'immatriculation de véhicule et le flou de mouvement de la plaque d'immatriculation de véhicule peut être efficacement éliminée, ce qui permet d'améliorer considérablement la précision de reconnaissance de plaques d'immatriculation de véhicule.
PCT/CN2020/071330 2020-01-10 2020-01-10 Procédé et appareil de reconnaissance de plaque d'immatriculation de véhicule, dispositif électronique et support d'enregistrement WO2021138893A1 (fr)

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CN202080095192.2A CN115298705A (zh) 2020-01-10 2020-01-10 车牌识别方法、装置、电子设备及存储介质

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CN116012833B (zh) * 2023-02-03 2023-10-10 脉冲视觉(北京)科技有限公司 车牌检测方法、装置、设备、介质和程序产品
CN116977949A (zh) * 2023-08-24 2023-10-31 北京唯行科技有限公司 车辆停车巡检方法、装置和设备

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