CN115909527A - Unattended ETC lane charging system and method based on video recognition technology - Google Patents
Unattended ETC lane charging system and method based on video recognition technology Download PDFInfo
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Abstract
The invention discloses an unattended ETC lane charging system and method based on video recognition technology, the system comprises a barrier gate, a weighing system, lane charging software and a vehicle ETC, and also comprises: the video identification module is used for identifying the accurate vehicle type and the number of axles of the vehicle to be charged and combining the license plate information to form a vehicle data queue; the objection judgment module is used for checking the accuracy of the information of the vehicle type and the axle number of the vehicle before charging and then charging; and the self-service interactive module is used for starting a secondary payment service flow and approving the primary charging data. The invention solves the problem of ETC vehicle charging error caused by shaft type data error of an unattended ETC lane toll station; the problems that ETC vehicles cannot normally pass due to abnormal data reasons when being used in an unattended ETC lane, and serious potential safety hazards exist when the vehicles need to back and turn lanes are solved; the problem of toll station special circumstances handle can only rely on manual handling, intervene untimely, the treatment effeciency is not high is solved.
Description
Technical Field
The invention relates to an unattended ETC lane charging system and method based on a video recognition technology, and belongs to the technical field of road and bridge ETC vehicle charging.
Background
ETC (Electronic Toll Collection) non-stop Toll Collection system is the most advanced road and bridge Toll Collection mode in the world at present. Through the special short-range communication of microwave between the vehicle-mounted electronic tag installed on the vehicle windshield and the microwave antenna on the ETC lane of the toll station, the computer networking technology and the bank are utilized to carry out background settlement processing, so that the aim of paying the road and bridge fees of the vehicle through the road and bridge toll station without parking is fulfilled.
In recent years, the application of ETC charging systems brings considerable economic benefits to expressways in China and also brings some new problems. Firstly, the ETC charging system has the problems of less fee deduction and more fee deduction; secondly, the transaction system has safety risk, and the transaction completion progress and completion condition cannot be verified in time; finally, fewer field arrangement personnel are needed, the management and control level is insufficient, the special situation is not rapidly processed, and the vehicle is often required to be backed up to replace the toll lane.
The existing ETC charging system lacks accurate and effective vehicle type, axle number detection means and verification flow in the process of outlet charging, so that the ETC transaction progress of the vehicle cannot be interacted on line in real time, blacklist information cannot be verified in time, and even the ETC transaction progress cannot be quickly interfered when special conditions occur, so that the whole passing efficiency and the passing safety of a charging station are influenced.
Disclosure of Invention
In order to solve the problems, the invention provides an unattended ETC lane charging system and method based on a video recognition technology, which can improve the efficiency of charging transaction and the traffic efficiency of roads.
The technical scheme adopted for solving the technical problems is as follows:
in a first aspect, an unattended ETC lane charging system provided by an embodiment of the present invention includes a barrier gate, a weighing system, lane charging software, a vehicle ETC, and further includes:
the video identification module is used for identifying the accurate vehicle type and the number of axles of the vehicle to be charged and combining the license plate information to form a vehicle data queue;
the objection judgment module is used for checking the accuracy of the vehicle type and axle number information of the vehicle before charging and then charging, so that the problem of ETC vehicle charging error caused by axle type data error is avoided;
and the self-service interactive module is used for starting a secondary payment service flow and approving the primary charging data.
As a possible implementation manner of this embodiment, the video identification module includes:
the system comprises a vehicle head detection module, a vehicle head detection module and a vehicle head recognition module, wherein the vehicle head detection module is used for performing frame-by-frame detection on a driven vehicle through a vehicle head camera by using a deep AI learning feature detection algorithm, extracting vehicle head features, triggering vehicle head snapshot when the vehicle is detected and the vehicle head position reaches a trigger line, simultaneously extracting a license plate position on an image, deducting a license plate image, performing OCR character recognition, recognizing license plate numbers and license plate colors, and generating a snapshot image, a license plate color image, a license plate binary image and structured data;
the vehicle body detection module is used for detecting whether a vehicle enters a detection area or not in a background modeling mode through a vehicle body camera, starting frame-by-frame detection after the vehicle is confirmed to enter, counting the number of axles by +1 when the detected wheel position reaches a trigger line, and counting the number of the acquired axles and the distance between the axles after the vehicle body camera detects that the vehicle has exited the detection area in the background modeling mode;
and the data summarizing module is used for carrying out statistical calculation, and generating a group of data by combining the extracted license plate data, wherein the data is used for recording the color of the license plate of the vehicle, the number of the license plate, the vehicle type, the number of axles and the characteristic data of the vehicle type.
As a possible implementation manner of this embodiment, the objection determination module includes:
the data receiving module is used for receiving and judging vehicle type and axle number information, weighing information and license plate information of a current pre-charged vehicle;
the data checking module is used for checking whether the license plate, the model, the axle number and the weighing information of the current pre-charged vehicle are different or not;
and the fee deducting module is used for carrying out fee deducting service processing.
As a possible implementation manner of this embodiment, the self-service interaction module is specifically configured to: in the process of fee deduction and release after automatically judging the objected vehicle, if the objected vehicle has objection to the information such as the charge amount, manual intervention can be carried out through a 'call field service' button arranged in the lane, relevant dispute information is verified after a worker arrives at the lane, when the fee deduction needs to be carried out again, the objected vehicle can be informed to carry out secondary payment through electronic code scanning or self-service ETC card swiping, and meanwhile, the background cancels the first charge data.
As a possible implementation manner of this embodiment, the system further includes an online transaction module, which is configured to, during a lane charging software transaction process, perform online verification of each process of the transaction and vehicle ETC information, perform the verification step by step according to a transaction flow, and feed back a result in time.
As a possible implementation manner of this embodiment, the online transaction module includes:
the data uploading module is used for sending a vehicle information inquiry command to the antenna soft core, the antenna soft core receives the inquiry command and then forwards the inquiry command to the RSU road side unit, the RSU road side unit starts to scan vehicle information and returns the vehicle information to the antenna soft core, and the antenna soft core uploads the vehicle information to lane charging software after receiving the information;
the blacklist judging module is used for calculating the passing fee and sending a fee deduction instruction to the antenna soft core, the antenna soft core is directly linked with ETC background vehicle blacklist information and judges whether the information is a blacklist, if the information is not the blacklist, the fee deduction instruction is sent to the RSU road side unit, and if not, the transaction is stopped;
and the clearing and settlement module is used for sending the deduction order information to the ETC issuer for clearing and settlement according to the deduction result returned by the RSU road side unit executing the deduction process, and informing the barrier to release the lever after the lane charging software receives the deduction success return result.
In a second aspect, an embodiment of the present invention provides an unattended ETC lane charging method based on a video recognition technology, where the unattended ETC lane charging system based on the video recognition technology is based on any one of the foregoing unattended ETC lane charging systems, and the method includes the following steps:
identifying the accurate vehicle type and the number of axles of the vehicle to be charged, and combining the license plate information to form a vehicle data queue;
weighing the vehicle;
checking that the information of the vehicle type and the number of axles of the vehicle is accurate and then charging according to the weighing information;
and performing online transaction according to the charging information.
As a possible implementation manner of this embodiment, the identifying an accurate vehicle type and an accurate number of axles of a vehicle to be charged, and forming a vehicle data queue by combining license plate information includes:
carrying out deep AI learning characteristic detection algorithm frame by frame detection on a driven vehicle through a vehicle head camera, extracting vehicle head characteristics, triggering vehicle head snapshot when the vehicle is detected and the vehicle head position reaches a trigger line, simultaneously extracting a license plate position on the image, deducting a license plate image, carrying out OCR character recognition, recognizing a license plate number and a license plate color, and generating a snapshot image, a license plate color image, a license plate binary image and structured data;
detecting whether a vehicle enters a detection area or not by a vehicle body camera in a background modeling mode, starting to detect frame by frame after the vehicle is confirmed to enter, counting the number of axles by +1 when the detected wheel position reaches a trigger line, and counting the obtained number of axles and the axle distance after the vehicle body camera detects that the vehicle exits the detection area in the background modeling mode;
and performing statistical calculation, and combining the extracted license plate data to generate a group of data for recording the color of the license plate of the vehicle, the number of the license plate, the vehicle type, the number of axles and the characteristic data of the vehicle type.
As a possible implementation manner of this embodiment, the checking that the vehicle model and axle number information of the vehicle is accurate and then charging according to the weighing information includes:
receiving and judging vehicle type and axle number information, weighing information and license plate information of a vehicle to be charged;
checking that the license plate, the model, the axle number and the weighing information of the current pre-charged vehicle are not different;
and carrying out fee deduction service processing.
As a possible implementation manner of this embodiment, the performing an online transaction according to the charging information includes:
the lane charging software sends a vehicle information query instruction to the antenna soft core, the antenna soft core receives the query instruction and then forwards the query instruction to the RSU road side unit, the RSU road side unit starts to scan vehicle information and returns the vehicle information to the antenna soft core, and the antenna soft core uploads the vehicle information to the lane charging software after receiving the information;
the lane charging software calculates the passing fee and sends a fee deduction instruction to the antenna soft core, the antenna soft core is directly linked with ETC background vehicle blacklist information and judges whether the information is a blacklist, if the information is not the blacklist, the fee deduction instruction is sent to the RSU road side unit, and if the information is not the blacklist, the transaction is stopped;
the RSU road side unit executes the fee deduction process and returns a fee deduction result, sends fee deduction order information to an ETC issuer for clearing and settlement, and informs the barrier gate of releasing after lane charging software receives the fee deduction success return result.
As a possible implementation manner of this embodiment, the method further includes the following steps:
and (4) approving the primary charging data and starting a secondary payment service flow.
As a possible implementation manner of this embodiment, the approving the first-time charging data and starting the second-time payment service flow includes:
in the process of fee deduction and release after automatically judging the objected vehicle, if the objected vehicle has objection to the information such as the charge amount, manual intervention can be carried out through a 'call field service' button arranged in the lane, relevant dispute information is verified after a worker arrives at the lane, when the fee deduction needs to be carried out again, the objected vehicle can be informed to carry out secondary payment through electronic code scanning or self-service ETC card swiping, and meanwhile, the background cancels the first charge data.
The technical scheme of the embodiment of the invention has the following beneficial effects:
the invention designs a processing mode of automatic judgment logic for the weighted vehicle objection: and adding video AI vehicle type recognition equipment at the front end of an exit lane of the toll station to recognize the accurate vehicle type and the number of axles of the pre-charged vehicle, forming a vehicle data queue by combining the license plate information, and pushing the vehicle data queue to lane charging software in time. The ETC vehicle charging error problem caused by axle type data errors is effectively avoided by checking the accurate information of the vehicle type and the axle number before charging and then charging.
The invention adopts the design of online transaction logic: in the lane charging software transaction process, all processes of the transaction and vehicle ETC information check are carried out on line, results are fed back in time, the processes are carried out step by step according to the transaction flow, and the integrity and the success rate of the transaction are ensured.
The invention adopts the design of self-service interactive processing: at the charging site, add "call field personnel" button, the driver can in time call artificial intervention through this button, designs the secondary business flow of collecting fee simultaneously, after artifical approval, can cancel the first charging data at the background, avoided the heavy circumstances such as passing of backing a car to take place, improved toll station whole pass efficiency and pass safety.
The invention solves the problem of ETC vehicle charging error caused by shaft type data error of an unattended ETC lane toll station; the problems that the ETC vehicle cannot normally pass due to abnormal charging reasons of data on an unattended ETC lane and has serious potential safety hazards when backing and turning the lane are solved; the problem that the success rate of transaction cannot be guaranteed because the completion condition of the transaction cannot be accurately verified in real time by the unattended ETC lane charging is solved; the problem of toll station special circumstances handle can only rely on manual handling, intervene untimely, the treatment effeciency is not high is solved.
Drawings
FIG. 1 is a schematic diagram illustrating an unattended ETC lane-charging system based on video recognition technology, according to an exemplary embodiment;
FIG. 2 is a flow diagram illustrating a method for unattended ETC lane charging based on video recognition technology in accordance with an exemplary embodiment;
fig. 3 is a schematic diagram illustrating a toll booth according to the present invention, according to an exemplary embodiment.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
in order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Moreover, the present disclosure may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
As shown in fig. 1 and fig. 3, an unattended ETC lane charging system based on a video recognition technology provided by an embodiment of the present invention includes a barrier gate, a weighing system, lane charging software, a vehicle ETC, and further includes:
the video identification module is used for identifying the accurate vehicle type and the number of axles of the vehicle to be charged and combining the license plate information to form a vehicle data queue;
the objection judgment module is used for checking the accuracy of the vehicle type and axle number information of the vehicle before charging and then charging, so that the problem of ETC vehicle charging error caused by axle type data error is avoided;
and the self-service interactive module is used for starting a secondary payment service flow and approving the primary charging data.
As a possible implementation manner of this embodiment, the video identification module includes:
the system comprises a vehicle head detection module, a vehicle head detection module and a vehicle head recognition module, wherein the vehicle head detection module is used for performing frame-by-frame detection on a driven vehicle through a vehicle head camera by using a deep AI learning feature detection algorithm, extracting vehicle head features, triggering vehicle head snapshot when the vehicle is detected and the vehicle head position reaches a trigger line, simultaneously extracting a license plate position on an image, deducting a license plate image, performing OCR character recognition, recognizing license plate numbers and license plate colors, and generating a snapshot image, a license plate color image, a license plate binary image and structured data;
the vehicle body detection module is used for detecting whether a vehicle enters a detection area or not in a background modeling mode through a vehicle body camera, starting frame-by-frame detection after the vehicle is confirmed to enter, counting the number of axles by +1 when the detected wheel position reaches a trigger line, and counting the number of the acquired axles and the distance between the axles after the vehicle body camera detects that the vehicle has exited the detection area in the background modeling mode;
and the data summarizing module is used for carrying out statistical calculation, and generating a group of data by combining the extracted license plate data, wherein the data is used for recording the color of the license plate of the vehicle, the number of the license plate, the vehicle type, the number of axles and the characteristic data of the vehicle type.
As a possible implementation manner of this embodiment, the objection judgment module includes:
the data receiving module is used for receiving and judging vehicle type and axle number information, weighing information and license plate information of a current pre-charged vehicle;
the data checking module is used for checking whether the license plate, the model, the axle number and the weighing information of the current pre-charged vehicle are different or not;
and the fee deduction module is used for carrying out fee deduction service processing.
As a possible implementation manner of this embodiment, the self-service interaction module is specifically configured to: in the process of fee deduction and release after automatically judging the objected vehicle, if the objected vehicle has objection to the information such as the charge amount, manual intervention can be carried out through a 'call field service' button arranged in the lane, relevant dispute information is verified after a worker arrives at the lane, when the fee deduction needs to be carried out again, the objected vehicle can be informed to carry out secondary payment through electronic code scanning or self-service ETC card swiping, and meanwhile, the background cancels the first charge data.
As a possible implementation manner of this embodiment, the system further includes an online transaction module, which is used for performing online verification of each transaction process and vehicle ETC information during the lane charging software transaction process, performing step-by-step verification according to the transaction flow, and feeding back the result in time.
As a possible implementation manner of this embodiment, the online transaction module includes:
the data uploading module is used for sending a vehicle information query instruction to the antenna soft core, the antenna soft core receives the query instruction and then forwards the query instruction to the RSU road side unit, the RSU road side unit starts to scan vehicle information and returns the vehicle information to the antenna soft core, and the antenna soft core uploads the vehicle information to lane charging software after receiving the information;
the blacklist judging module is used for calculating the passing fee and sending a fee deduction instruction to the antenna soft core, the antenna soft core is directly linked with ETC background vehicle blacklist information and judges whether the information is a blacklist, if the information is not the blacklist, the fee deduction instruction is sent to the RSU road side unit, and if the information is not the blacklist, the transaction is stopped;
and the clearing and settlement module is used for sending the fee deduction order information to an ETC issuer for clearing and settlement according to the fee deduction result returned by the RSU road side unit executing the fee deduction process, and informing the barrier gate to release after the lane charging software receives the fee deduction success return result.
As shown in fig. 2 and fig. 3, an embodiment of the present invention provides an unattended ETC lane charging method based on a video recognition technology, where the unattended ETC lane charging system based on the video recognition technology is based on any one of the foregoing methods, and the method includes the following steps:
step 1, identifying the accurate vehicle type and the number of axles of the vehicle to be charged, and combining license plate information to form a vehicle data queue.
Specifically, the identifying the accurate vehicle type and the number of axles of the vehicle to be charged and combining the license plate information to form a vehicle data queue includes:
carrying out deep AI learning characteristic detection algorithm frame by frame detection on a driven vehicle through a vehicle head camera, extracting vehicle head characteristics, triggering vehicle head snapshot when the vehicle is detected and the vehicle head position reaches a trigger line, simultaneously extracting a license plate position on the image, deducting a license plate image, carrying out OCR character recognition, recognizing a license plate number and a license plate color, and generating a snapshot image, a license plate color image, a license plate binary image and structured data;
detecting whether a vehicle enters a detection area or not by a vehicle body camera in a background modeling mode, starting to detect frame by frame after the vehicle is confirmed to enter, counting the number of axles by +1 when the detected wheel position reaches a trigger line, and counting the obtained number of axles and the axle distance after the vehicle body camera detects that the vehicle exits the detection area in the background modeling mode;
and performing statistical calculation, and combining the extracted license plate data to generate a group of data for recording the color of the license plate of the vehicle, the number of the license plate, the vehicle type, the axle number and the characteristic data of the vehicle type.
The invention adopts a video AI recognition technology to recognize and process the license plate, the model and the number of axles of the vehicle, and uploads the recognition result of the license plate, the model (number of axles) and other information of the vehicle to lane charging software before the weighing data is uploaded.
And 2, weighing the vehicle.
When the vehicle passes through the weighing platform, the weighing platform uploads the weighing data to the vehicle charging software.
And 3, checking that the information of the vehicle type and the axle number of the vehicle is accurate, and then charging according to the weighing information.
As a possible implementation manner of this embodiment, the checking that the vehicle model and the axle number information of the vehicle are accurate and then charging according to the weighing information includes:
and step 31, receiving and judging the vehicle type and axle number information, the weighing information and the license plate information of the vehicle to be charged.
Step 32, checking that the license plate, the model, the number of axles and the weighing information of the current pre-paid vehicle are not different;
specifically, after receiving license plate information (a recognition result of a rear license plate recognition instrument) of a vehicle which is subject to pre-charging currently, lane charging software searches vehicle type (axle type) information and a queue number of a corresponding vehicle according to the license plate information, such as pre-charging vehicle license plate number 'Huang Lu A12345' and the queue number 'No. 0012', finds vehicle type (axle type) data and the queue number of the license plate number in a vehicle type (axle type) information queue, such as vehicle type (axle type) data '127 vehicle type (6 axle) ", vehicle type' truck ', queue number' No.0012 'and the like, and then asks for weighing information related data according to the queue number, such as vehicle type (axle type) data' 127 vehicle type (6 axle)", total weight 45t and the like. The whole process checks the correctness of the queue of the pre-charged vehicles (obtained by referring to the uniqueness of the license plate and/or the queue number) and the correctness of vehicle type (axle type) data (obtained by comparing vehicle type (axle type) information and weighing information), thereby realizing the self-sending judgment of vehicle data objection, wherein any one of the four key parameters of the license plate number, the queue number, the vehicle type and the axle type is checked to be different, and the automatic fee deduction process can not be entered.
And step 33, carrying out fee deduction service processing.
After receiving the comprehensive discrimination of vehicle type (axle type) information, weighing information and license plate information (recognition result of a rear license plate recognition instrument) of the current pre-charged vehicle, the lane charging software verifies that the license plate, the vehicle type (axle number) and the weighing information of the current pre-charged vehicle are not different, and then carries out service processing of relevant processes such as fee deduction and the like, thereby realizing the core function of automatic discrimination of the occurrence of objections of the weighted vehicle. The method has the advantages that the vehicle type and axle number information of the vehicle are checked before charging is accurate, and then charging is carried out, so that the problem of ETC vehicle charging errors caused by axle type data errors of an unattended ETC lane toll station is solved; the problems that ETC vehicles cannot normally pass due to abnormal data reasons when being used in an unattended ETC lane, and serious potential safety hazards exist when the vehicles need to back and turn lanes are solved;
and 4, performing online transaction according to the charging information.
As a possible implementation manner of this embodiment, the performing an online transaction according to the charging information includes:
the lane charging software sends a vehicle information query instruction to the antenna soft core, the antenna soft core receives the query instruction and then forwards the query instruction to the RSU road side unit, the RSU road side unit starts to scan vehicle information and returns the vehicle information to the antenna soft core, and the antenna soft core uploads the vehicle information to the lane charging software after receiving the information;
the lane charging software calculates the passing fee and sends a fee deduction instruction to the antenna soft core, the antenna soft core is directly linked with ETC background vehicle blacklist information and judges whether the information is a blacklist, if the information is not the blacklist, the fee deduction instruction is sent to the RSU road side unit, and if the information is not the blacklist, the transaction is stopped;
the RSU road side unit executes the fee deduction process and returns a fee deduction result, sends fee deduction order information to an ETC issuer for clearing and settlement, and informs the barrier gate of releasing after lane charging software receives the fee deduction success return result.
In the lane charging software transaction process, all transaction processes and vehicle ETC information check are carried out on line, results are fed back in time, and the transaction processes are carried out step by step to ensure the integrity and success rate of the transaction; the problem of unmanned on duty ETC lane charge can't real-time accurate verify the transaction condition of accomplishing, consequently can't guarantee the success rate of trading is solved.
As a possible implementation manner of this embodiment, the method further includes the following steps:
and (4) approving the primary charging data and starting a secondary payment service flow.
As a possible implementation manner of this embodiment, the approving the first-time charging data and starting the second-time payment service flow includes:
in the process of fee deduction and release after automatically judging the objected vehicle, if the objected vehicle has objection to the information such as the charge amount, manual intervention can be carried out through a 'call field service' button arranged in the lane, relevant dispute information is verified after a worker arrives at the lane, when the fee deduction needs to be carried out again, the objected vehicle can be informed to carry out secondary payment through electronic code scanning or self-service ETC card swiping, and meanwhile, the background cancels the first charge data.
At the charging site, add "call field service" button, the driver can in time call artificial intervention through this button, designs the secondary business flow of collecting fee simultaneously, after artifical approval, can cancel the first charge data at the background, avoid the circumstances such as the heavy traffic of falling to take place, improve the whole current efficiency and the current safety of toll station, solved the special condition of toll station and handled and can only rely on artifical the processing, intervene untimely, the problem that the treatment effeciency is not high.
According to the invention, the combination of a common road and bridge weight-metering charging mode and an unattended ETC charging mode is realized by a system integration means in combination with technologies such as blacklist cloud comparison and license plate number redundancy comparison, and the problems of large dissimilarity and low passing efficiency of truck passing ETC lane weight metering are solved.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. The utility model provides an unmanned on duty ETC lane charging system based on video identification technique, includes banister, weighing system, lane charge software and vehicle ETC, its characterized in that still includes:
the video identification module is used for identifying the accurate vehicle type and the number of axles of the vehicle to be charged and combining the license plate information to form a vehicle data queue;
the objection judgment module is used for checking the accuracy of the vehicle type and axle number information of the vehicle before charging and then charging, so that the problem of ETC vehicle charging error caused by axle type data error is avoided;
and the self-service interactive module is used for starting a secondary payment service flow and approving the primary charging data.
2. The video recognition technology-based unattended ETC lane charging system according to claim 1, wherein the video recognition module comprises:
the vehicle head detection module is used for carrying out deep AI learning feature detection algorithm frame by frame detection on a driven vehicle through a vehicle head camera, extracting vehicle head features, triggering vehicle head snapshot when the vehicle is detected and the vehicle head position reaches a trigger line, simultaneously extracting the license plate position on the image, deducting a license plate image, carrying out OCR character recognition, recognizing license plate numbers and license plate colors, and generating a snapshot image, a license plate color image, a license plate binary image and structured data;
the vehicle body detection module is used for detecting whether a vehicle enters a detection area or not in a background modeling mode through a vehicle body camera, starting frame-by-frame detection after the vehicle is confirmed to enter, counting the number of axles by +1 when the detected wheel position reaches a trigger line, and counting the number of the acquired axles and the distance between the axles after the vehicle body camera detects that the vehicle has exited the detection area in the background modeling mode;
and the data summarizing module is used for carrying out statistical calculation, and generating a group of data by combining the extracted license plate data, wherein the data is used for recording the color of the license plate of the vehicle, the number of the license plate, the vehicle type, the number of axles and the characteristic data of the vehicle type.
3. The video recognition technology-based unattended ETC lane charging system according to claim 1, wherein the objection discrimination module comprises:
the data receiving module is used for receiving and judging vehicle type and axle number information, weighing information and license plate information of a current pre-charged vehicle;
the data checking module is used for checking whether the license plate, the model, the axle number and the weighing information of the current pre-charging vehicle are different;
and the fee deduction module is used for carrying out fee deduction service processing.
4. The video recognition technology-based unattended ETC lane charging system according to claim 1, wherein the self-service interaction module is specifically configured to: in the process of fee deduction and release after automatically judging the objected vehicle, if the objected vehicle has objection to the information such as the charge amount, manual intervention can be carried out through a 'call field service' button arranged in the lane, relevant dispute information is verified after a worker arrives at the lane, when the fee deduction needs to be carried out again, the objected vehicle can be informed to carry out secondary payment through electronic code scanning or self-service ETC card swiping, and meanwhile, the background cancels the first charge data.
5. The unattended ETC lane charging system based on the video recognition technology according to any one of claims 1-4, characterized by further comprising an online transaction module, wherein the online transaction module is used for conducting all processes of transaction and vehicle ETC information check on line in the lane charging software transaction process, conducting the processes step by step according to the transaction flow and feeding back the result in time.
6. An unattended ETC lane charging method based on a video recognition technology, which is characterized in that the unattended ETC lane charging system based on the video recognition technology according to any one of claims 1 to 5, the method comprises the following steps:
identifying the accurate vehicle type and the number of axles of the vehicle to be charged, and forming a vehicle data queue by combining license plate information;
weighing the vehicle;
checking that the information of the vehicle type and the axle number of the vehicle is accurate and then charging according to the weighing information;
and performing online transaction according to the charging information.
7. The video recognition technology-based unattended ETC lane charging method according to claim 6, wherein the identifying of the accurate vehicle type and number of axles of the vehicle to be charged and the formation of the vehicle data queue in combination with the license plate information comprises:
carrying out deep AI learning characteristic detection algorithm frame by frame detection on a driven vehicle through a vehicle head camera, extracting vehicle head characteristics, triggering vehicle head snapshot when the vehicle is detected and the vehicle head position reaches a trigger line, simultaneously extracting a license plate position on the image, deducting a license plate image, carrying out OCR character recognition, recognizing a license plate number and a license plate color, and generating a snapshot image, a license plate color image, a license plate binary image and structured data;
the method comprises the steps that a vehicle body camera detects whether a vehicle enters a detection area or not in a background modeling mode, frame-by-frame detection is started after the vehicle is confirmed to enter, the number of axles is +1 when the detected wheel position reaches a trigger line, and the number of the axles and the distance between the axles are counted after the vehicle body camera detects that the vehicle exits the detection area in the background modeling mode;
and performing statistical calculation, and combining the extracted license plate data to generate a group of data for recording the color of the license plate of the vehicle, the number of the license plate, the vehicle type, the number of axles and the characteristic data of the vehicle type.
8. The video recognition technology-based unattended ETC lane charging method according to claim 6, wherein the vehicle type and axle number information of the verified vehicle is accurate and then the vehicle is charged according to weighing information, and the method comprises the following steps:
receiving and judging vehicle type and axle number information, weighing information and license plate information of a vehicle to be charged;
checking that the license plate, the model, the axle number and the weighing information of the current pre-charged vehicle are not different;
and carrying out fee deduction service processing.
9. The video recognition technology-based unattended ETC lane charging method according to claim 6, wherein the online transaction according to the charging information comprises:
the lane charging software sends a vehicle information query instruction to the antenna soft core, the antenna soft core receives the query instruction and then forwards the query instruction to the RSU road side unit, the RSU road side unit starts to scan vehicle information and returns the vehicle information to the antenna soft core, and the antenna soft core uploads the vehicle information to the lane charging software after receiving the information;
the lane charging software calculates the passing fee and sends a fee deduction instruction to the antenna soft core, the antenna soft core is directly linked with ETC background vehicle blacklist information and judges whether the information is a blacklist, if the information is not the blacklist, the fee deduction instruction is sent to the RSU road side unit, and if the information is not the blacklist, the transaction is stopped;
the RSU road side unit executes the fee deduction process and returns a fee deduction result, sends fee deduction order information to the ETC issuer for clearing and settlement, and informs the barrier gate of releasing the lever after the lane charging software receives the fee deduction success return result.
10. The unattended ETC lane charging method based on the video recognition technology according to any one of claims 6-9, further comprising the steps of:
and (4) approving the primary charging data and starting a secondary payment service flow.
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