Intelligent garage gate control system and method applying image analysis technology

Abstract

The invention discloses a smart garage gate control system and method applying an image analysis technology, and belongs to the technical field of image analysis, and the method comprises the following steps: collecting an image, obtaining a grayscale image, and analyzing comprehensive grayscale values corresponding to different colors; extracting the contour of the vehicle in the grayscale image, and training a deep learning model; collecting an image when a vehicle to be put in storage arrives at the position of the gate, and obtaining a gray level image; vehicle information of the vehicle is analyzed and recorded, a gate is controlled to be opened, and the vehicle enters a garage; cameras in the garage collect images of passing vehicles, and whether the passing vehicles in the images collected by the cameras are the same vehicle or not is judged; judging whether the passing vehicle leaves the garage or not, predicting the time when the vehicle arrives at the gate, and controlling the gate to open in advance, so that the vehicle leaves the garage; according to the invention, the identification of the vehicle is further limited, the identification accuracy is improved, the out-of-garage time of the vehicle is predicted, and the passing efficiency of the vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image analysis, and in particular to a smart garage gate control system and method using image analysis technology. Background Art

[0002] As people's living standards improve, the number of cars is increasing. The garage gate control system is an important part of the intelligent parking system. It is mainly used to control the entry and exit of vehicles in the garage and improve the efficiency and safety of vehicle management.

[0003] The garage gate machine uses a camera to identify the license plate of the vehicle entering and exiting, and then raises and lowers the gate. However, the camera usually requires the vehicle to reach a certain distance from the gate machine before it can be identified. The identification process is often too slow or inaccurate, causing vehicle congestion. This not only reduces the efficiency of vehicle travel and brings inconvenience to vehicle travel, but also interferes with the vehicle management of the garage due to inaccurate identification, reducing the practicality of the gate machine.

[0004] Therefore, people are in urgent need of a smart garage gate control system that uses image analysis technology to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a smart garage door gate control system using image analysis technology to solve the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A smart garage gate control method using image analysis technology, the method comprising the following steps:

[0008] S1. According to the vehicle models, collect images of vehicles of different models at different angles, perform grayscale processing on the images, and obtain grayscale images; according to the color of the vehicle in each image, combined with the grayscale value of the pixel points in each grayscale image, analyze the comprehensive grayscale values ​​corresponding to different colors;

[0009] S2. Use image analysis technology to extract the outline of the vehicle in the grayscale image, establish a deep learning model, take the shooting angle of the image and the outline of the vehicle as input, and take the model corresponding to the vehicle in the image as output to train the deep learning model;

[0010] S3, collect the image of the vehicle to be entered when it arrives at the gate position, grayscale process the image, and obtain the grayscale image; use optical character recognition technology to analyze the grayscale image to obtain the license plate number of the vehicle; extract the outline of the vehicle in the grayscale image, and obtain the model of the vehicle through the deep learning model in combination with the shooting angle of the image; obtain the comprehensive grayscale value of the vehicle in the grayscale image and match the color of the vehicle; record the vehicle information of the vehicle and control the gate to open, and the vehicle enters the warehouse, the vehicle information includes the license plate number, model and color;

[0011] S4, each camera in the garage collects images of passing vehicles, analyzes vehicle information of the passing vehicles in the images collected by each camera, and determines whether the passing vehicles in the images collected by each camera are the same vehicle;

[0012] S5. According to the time when each camera collects images and the distance between the corresponding camera and the gate, determine whether the passing vehicle is leaving the warehouse and predict the time when the vehicle arrives at the gate, control the gate to open in advance, and the vehicle leaves the warehouse.

[0013] According to the above technical solution, the specific process of step S1 is as follows:

[0014] S1-1. Establish a coordinate system with the center of the vehicle as the origin, the straight line where the center axis of the vehicle is located as the x-axis, the straight line perpendicular to the x-axis and passing through the origin as the y-axis, and the straight line perpendicular to the x-axis and y-axis and passing through the origin as the z-axis. The angle between the camera and the origin in the coordinate system is used as the shooting angle.

[0015] S1-2, collect N1 images, and record the ath image as T a，b，θ , T a，b，θ Indicates that the a-th image is an image of the b-th vehicle model taken at a shooting angle of θ; N1 is a positive integer; a∈[1, N2], N2 is a positive integer; b∈[1, N3], N3 is a positive integer; θ∈[-2π, -2π];

[0016] T a，b，θ The intensity value of the red channel of the cth pixel in is recorded as T a，b，θ The intensity value of the green channel of the cth pixel in is recorded as T a，b，θ The intensity value of the blue channel of the cth pixel in is recorded as N4 is a positive integer;

[0017] To T a，b，θ Perform grayscale processing to obtain the grayscale image of the a-th image, denoted as Tgary a，b，θ , Tgary a，b，θ The grayscale image representing the ath image is the grayscale image of the image of the bth vehicle model taken at a shooting angle of θ;

[0018] The grayscale value of a pixel in a grayscale image is calculated according to the following formula:

[0019]

[0020] in, Represents Tgary a，b，θ The gray value of the cth pixel in ; α1, α2 and α3 are fixed parameters preset by the system;

[0021] S1-3, the color of the vehicle in the ath image is the dth color, d∈[1,N5], N5 is a positive integer;

[0022] About Tgary a，b，θ The gray value of each pixel in is screened to select Tgary a，b，θ The most frequent As the gray value corresponding to the dth color;

[0023] Get the gray value corresponding to the dth color in the N1 images collected, and take the average gray value corresponding to the dth color in each image as the comprehensive gray value corresponding to the dth color, recorded as Igary d ;

[0024] Since the main color of the vehicle occupies the largest area of ​​the vehicle body, the grayscale value that appears most times is selected as the grayscale value corresponding to the color. In addition, there is a deviation in the grayscale value of the same color in different pictures, but it is very small. It is more accurate to use the comprehensive grayscale value for judgment.

[0025] According to the above technical solution, the specific process of step S2 is as follows:

[0026] S2-1. Extract Tgary using image recognition technology a，b，θ The contour of the vehicle in the a-th image is obtained, and the contour point set of the vehicle in the grayscale image is recorded as the set

[0027] S2-2. Establish a deep learning model and transform θ and As input, the bth car model is used as output to train the deep learning model;

[0028] By analyzing different shooting angles of vehicles of the same model and integrating the contour features of the vehicle at different angles, different cameras can capture images of the vehicle at different angles, and the vehicle model can be accurately identified through a deep learning network.

[0029] According to the above technical solution, the specific process of step S3 is as follows:

[0030] S3-1, the time when the waiting vehicle arrives at the gate position is recorded as time t1; the waiting vehicle arriving at the gate position at time t1 is recorded as car t1 ; The image captured at time t1 is recorded as The shooting angle is right Perform grayscale processing to obtain a grayscale image, denoted as

[0031] S3-2. Using optical character recognition technology Perform analysis and obtain car t1 The license plate number is recorded as

[0032] S3-3. Extraction car t1 The outline of Combination Get car through deep learning model t1 The model of

[0033] S3-4, through The intensity values ​​of the red, green, and blue channels of each pixel in The gray value of each pixel in The gray value of each pixel in is filtered to select The gray value that appears most often in the t1 The comprehensive gray value is recorded as

[0034] like satisfy car t1 The color is the dth color; α4 is a fixed parameter preset by the system;

[0035] like Dissatisfied car t1 The color of is not the d-th color;

[0036] S3-5, record car t1 of and color and control the gate opening.

[0037] According to the above technical solution, the specific process of step S4 is as follows:

[0038] S4-1, each camera in the garage collects images of passing vehicles, and arranges the cameras from earliest to latest in the order of collection time;

[0039] The time when the e-th camera captures the image of the passing vehicle is recorded as t2 e , e∈[1,N6], N6 is a positive integer;

[0040] According to t2 e Arrange the cameras from the earliest to the latest moment in time;

[0041] Filter out cameras that meet the following conditions:

[0042]

[0043] Among them, α5 and α6 are fixed parameters preset by the system;

[0044] The time when the fth camera captures the image of the passing vehicle is recorded as t3 f , f∈[1,N7], N7 is a positive integer and N7≤N6; set t3 f The vehicle passing the fth camera at time is denoted as T3 f The image captured by the fth camera at time is recorded as The shooting angle is

[0045] S4-2. Acquisition middle of and color, among which, express The license plate number; express Model of car;

[0046] Will of and color, and car t1 of and color matching;

[0047] like of and color are the same as car t1 of If the color is the same, the camera captures the image With car t1 It’s the same car;

[0048] Filter out the collected cars t1 The cameras of the images are rearranged in the order of acquisition time from earliest to latest;

[0049] The g-th camera captures the image to car t1 The moment of the image is recorded as t4 g , g∈[1,N8], N8 is a positive integer and N8≤N7;

[0050] Increase the judgment of vehicle model and color, and effectively prevent photo deception. At the same time, the vehicle model and color are significant visual features. When used in combination with license plate information, it can greatly improve the accuracy of recognition and reduce misjudgments and missed judgments.

[0051] According to the above technical solution, the specific process of step S5 is as follows:

[0052] S5-1. Let the distance between the gth camera and the gate be L. g ;

[0053] If L g >L g+1 , then the passing vehicle leaves the warehouse;

[0054] If L g ≤L g+1 , then the passing vehicles will not leave the warehouse;

[0055] Among them, the N8+1th camera represents the camera at the gate and L N8+1 =0;

[0056] S5-2. Predict the time when the vehicle arrives at the gate according to the following formula:

[0057]

[0058] in, Indicates car t1 Predicted time of arrival at the gate;

[0059] S5-3, advance control of the gate When it is turned on, the vehicle leaves the warehouse.

[0060] A smart garage gate control system using image analysis technology, characterized in that the system includes an information acquisition module, an information processing module and an execution module;

[0061] The information acquisition module is used to acquire the information required by the system; the information processing module is used to store, analyze and transmit the information of each module; and the execution module is used to execute the information of the information processing module.

[0062] According to the above technical solution, the information collection module includes a vehicle information unit and a garage information unit;

[0063] The vehicle information unit is used to collect data information of the vehicle, and the garage information unit is used to collect data information of the camera in the garage.

[0064] According to the above technical solution, the information processing module includes an information storage unit, an information analysis unit and an information transmission unit;

[0065] The information storage unit is used to store the information acquired by the information acquisition module; the information analysis unit is used to analyze the information acquired by the information acquisition module; and the information transmission unit is used for information transmission of each module in the system.

[0066] According to the above technical solution, the information display module includes a display unit and a gate control unit;

[0067] The display unit is used to display the information of vehicles entering and exiting the garage, and the gate control unit is used to control the raising and lowering of the garage gate.

[0068] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0069] The present invention analyzes the image of the vehicle and, when identifying the vehicle, not only identifies the license plate number of the vehicle, but also identifies the model and color of the vehicle, further restricts the identification of the vehicle, improves the accuracy and safety of identification, and by identifying the movement path of the vehicle, predicts the vehicle's exit time, controls the opening of the gate in advance, improves the efficiency of vehicle passage, and enhances the management of the garage and the practicality of the gate. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0071] Figure 1 It is a flow chart of a smart garage gate control method using image analysis technology according to the present invention;

[0072] Figure 2 It is a structural schematic diagram of a smart garage gate control system using image analysis technology in the present invention. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] See also Figure 1 , the present invention provides a technical solution:

[0075] S1. According to the vehicle models, collect images of vehicles of different models at different angles, perform grayscale processing on the images, and obtain grayscale images; according to the color of the vehicle in each image, combined with the grayscale value of the pixel points in each grayscale image, analyze the comprehensive grayscale values ​​corresponding to different colors;

[0076] The specific process of step S1 is as follows:

[0077] S1-1. Establish a coordinate system with the center of the vehicle as the origin, the straight line where the center axis of the vehicle is located as the x-axis, the straight line perpendicular to the x-axis and passing through the origin as the y-axis, and the straight line perpendicular to the x-axis and y-axis and passing through the origin as the z-axis. The angle between the camera and the origin in the coordinate system is used as the shooting angle.

[0078] Define the shooting angle to provide a unified analysis standard for subsequent data collection and analysis, thereby improving the accuracy of data analysis;

[0079] S1-2, collect N1 images, and record the ath image as T a，b，θ , T a，b，θ Indicates that the a-th image is an image of the b-th vehicle model taken at a shooting angle of θ; N1 is a positive integer; a∈[1, N2], N2 is a positive integer; b∈[1, N3], N3 is a positive integer; θ∈[-2π, -2π];

[0080] T a，b，θ The intensity value of the red channel of the cth pixel in is recorded as T a，b，θ The intensity value of the green channel of the cth pixel in is recorded as T a，b，θ The intensity value of the blue channel of the cth pixel in is recorded as N4 is a positive integer;

[0081] To T a，b，θ Perform grayscale processing to obtain the grayscale image of the a-th image, denoted as Tgary a，b，θ , Tgary a，b，θ The grayscale image representing the ath image is the grayscale image of the image of the bth vehicle model taken at a shooting angle of θ;

[0082] The grayscale value of a pixel in a grayscale image is calculated according to the following formula:

[0083]

[0084] in, Represents Tgary a，b，θ The gray value of the cth pixel in ; α1, α2 and α3 are fixed parameters preset by the system;

[0085] For example:

[0086] T a，b，θ The intensity value of the red channel of the cth pixel in is recorded as T a，b，θ The intensity value of the green channel of the cth pixel in is recorded as T a，b，θ The intensity value of the blue channel of the cth pixel in is recorded as α1=0.299; α2=0.587; α3=0.114;

[0087] The grayscale value of a pixel in a grayscale image is calculated according to the following formula:

[0088]

[0089] get

[0090] S1-3, the color of the vehicle in the ath image is the dth color, d∈[1,N5], N5 is a positive integer;

[0091] About Tgary a，b，θ The gray value of each pixel in is screened to select Tgary a，b，θ The most frequent As the gray value corresponding to the dth color;

[0092] Get the gray value corresponding to the dth color in the N1 images collected, and take the average gray value corresponding to the dth color in each image as the comprehensive gray value corresponding to the dth color, recorded as Igary d ;

[0093] Since the main color of the vehicle occupies the largest area of ​​the vehicle body, the grayscale value that appears most times is selected as the grayscale value corresponding to the color. In addition, there is a deviation in the grayscale value of the same color in different pictures, but it is very small. It is more accurate to use the comprehensive grayscale value for judgment.

[0094] S2. Use image analysis technology to extract the outline of the vehicle in the grayscale image, establish a deep learning model, take the shooting angle of the image and the outline of the vehicle as input, and take the model corresponding to the vehicle in the image as output to train the deep learning model;

[0095] The specific process of step S2 is as follows:

[0096] S2-1. Extract Tgary using image recognition technology a，b，θ The contour of the vehicle in the a-th image is obtained, and the contour point set of the vehicle in the grayscale image is recorded as the set

[0097] S2-2. Establish a deep learning model and transform θ and As input, the bth car model is used as output to train the deep learning model;

[0098] By analyzing different shooting angles of vehicles of the same model and integrating the contour features of the vehicle at different angles, different cameras can capture images of the vehicle at different angles, and the vehicle model can be accurately identified through a deep learning network.

[0099] S3, collect the image of the vehicle to be entered when it arrives at the gate position, grayscale process the image, and obtain the grayscale image; use optical character recognition technology to analyze the grayscale image to obtain the license plate number of the vehicle; extract the outline of the vehicle in the grayscale image, and obtain the model of the vehicle through the deep learning model in combination with the shooting angle of the image; obtain the comprehensive grayscale value of the vehicle in the grayscale image and match the color of the vehicle; record the vehicle information of the vehicle and control the gate to open, and the vehicle enters the warehouse, the vehicle information includes the license plate number, model and color;

[0100] The specific process of step S3 is as follows:

[0101] S3-1, the time when the waiting vehicle arrives at the gate position is recorded as time t1; the waiting vehicle arriving at the gate position at time t1 is recorded as car t1 ; The image captured at time t1 is recorded as The shooting angle is right Perform grayscale processing to obtain a grayscale image, denoted as

[0102] S3-2. Using optical character recognition technology Perform analysis and obtain car t1 The license plate number is recorded as

[0103] S3-3. Extraction car t1 The outline of Combination Get car through deep learning model t1 The model of

[0104] S3-4, through The intensity values ​​of the red, green, and blue channels of each pixel in The gray value of each pixel in The gray value of each pixel in is filtered to select The gray value that appears most often in the t1The comprehensive gray value is recorded as

[0105] The images obtained by the camera at the gate are used to analyze and record the vehicle information of the incoming vehicles, providing data support for subsequent vehicle exit and prediction of the time when the vehicle arrives at the gate;

[0106] like satisfy car t1 The color is the dth color; α4 is a fixed parameter preset by the system;

[0107] like Dissatisfied car t1 The color of is not the d-th color;

[0108] S3-5, record car t1 of and color and control the gate opening.

[0109] S4, each camera in the garage collects images of passing vehicles, analyzes vehicle information of the passing vehicles in the images collected by each camera, and determines whether the passing vehicles in the images collected by each camera are the same vehicle;

[0110] The specific process of step S4 is as follows:

[0111] S4-1, each camera in the garage collects images of passing vehicles, and arranges the cameras from earliest to latest in the order of collection time;

[0112] The time when the e-th camera captures the image of the passing vehicle is recorded as t2 e , e∈[1,N6], N6 is a positive integer;

[0113] According to t2 e Arrange the cameras from the earliest to the latest moment in time;

[0114] Filter out cameras that meet the following conditions:

[0115]

[0116] Among them, α5 and α6 are fixed parameters preset by the system;

[0117] The time when the fth camera captures the image of the passing vehicle is recorded as t3 f , f∈[1,N7], N7 is a positive integer and N7≤N6; set t3 f The vehicle passing the fth camera at time is denoted as T3 f The image captured by the fth camera at time is recorded as The shooting angle is

[0118] S4-2. Acquisition middle of and color, among which, express The license plate number; express Model of car;

[0119] Will of and color, and car t1 of and color matching;

[0120] like of and color are the same as car t1 of If the color is the same, the camera captures the image With car t1 It’s the same car;

[0121] Filter out the collected cars t1 The cameras of the images are rearranged in the order of acquisition time from earliest to latest;

[0122] The g-th camera captures the image to car t1 The moment of the image is recorded as t4 g , g∈[1,N8], N8 is a positive integer and N8≤N7;

[0123] Increase the judgment of vehicle model and color, and effectively prevent photo deception. At the same time, the vehicle model and color are significant visual features. When used in combination with license plate information, it can greatly improve the accuracy of recognition and reduce misjudgments and missed judgments.

[0124] S5. According to the time when each camera collects images and the distance between the corresponding camera and the gate, determine whether the passing vehicle is leaving the warehouse and predict the time when the vehicle arrives at the gate, control the gate to open in advance, and the vehicle leaves the warehouse;

[0125] The specific process of step S5 is as follows:

[0126] S5-1. Let the distance between the gth camera and the gate be L. g ;

[0127] If L g >L g+1 , then the passing vehicle leaves the warehouse;

[0128] If L g ≤Lg+1 , then the passing vehicles will not leave the warehouse;

[0129] Among them, the N8+1th camera represents the camera at the gate and L N8+1 =0;

[0130] S5-2. Predict the time when the vehicle arrives at the gate according to the following formula:

[0131]

[0132] in, Indicates car t1 Predicted time of arrival at the gate;

[0133] S5-3, advance control of the gate When it is turned on, the vehicle leaves the warehouse;

[0134] For example:

[0135] t N8 =10:20:30;t41=10:20:00;L 1＝8 =200;L N8 =100;

[0136] The time it takes for a vehicle to arrive at the gate is predicted using the following formula:

[0137]

[0138] get

[0139] Control the gate in advance When it is turned on, the vehicle leaves the warehouse.

[0140] See also Figure 2 ,A smart garage gate control system using image analysis technology, characterized in that: the system includes an information acquisition module, an information processing module and an execution module;

[0141] The information acquisition module is used to acquire the information required by the system; the information processing module is used to store, analyze and transmit the information of each module; and the execution module is used to execute the information of the information processing module.

[0142] According to the above technical solution, the information collection module includes a vehicle information unit and a garage information unit;

[0143] The vehicle information unit is used to collect data information of the vehicle, and the garage information unit is used to collect data information of the camera in the garage.

[0144] According to the above technical solution, the information processing module includes an information storage unit, an information analysis unit and an information transmission unit;

[0145] The information storage unit is used to store the information acquired by the information acquisition module; the information analysis unit is used to analyze the information acquired by the information acquisition module; and the information transmission unit is used for information transmission of each module in the system.

[0146] According to the above technical solution, the information display module includes a display unit and a gate control unit;

[0147] The display unit is used to display the information of vehicles entering and exiting the garage, and the gate control unit is used to control the raising and lowering of the garage gate.

[0148] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0149] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A smart garage gate control method using image analysis technology, characterized in that: The method comprises the following steps: S1. According to the vehicle models, collect images of vehicles of different models at different angles, perform grayscale processing on the images, and obtain grayscale images; according to the color of the vehicle in each image, combined with the grayscale value of the pixel points in each grayscale image, analyze the comprehensive grayscale values ​​corresponding to different colors; S2. Use image analysis technology to extract the outline of the vehicle in the grayscale image, establish a deep learning model, take the shooting angle of the image and the outline of the vehicle as input, and take the model corresponding to the vehicle in the image as output to train the deep learning model; S3, collect the image of the vehicle to be entered when it arrives at the gate position, grayscale process the image, and obtain the grayscale image; use optical character recognition technology to analyze the grayscale image to obtain the license plate number of the vehicle; extract the outline of the vehicle in the grayscale image, and obtain the model of the vehicle through the deep learning model in combination with the shooting angle of the image; obtain the comprehensive grayscale value of the vehicle in the grayscale image and match the color of the vehicle; record the vehicle information of the vehicle and control the gate to open, and the vehicle enters the warehouse, the vehicle information includes the license plate number, model and color; S4, each camera in the garage collects images of passing vehicles, analyzes vehicle information of the passing vehicles in the images collected by each camera, and determines whether the passing vehicles in the images collected by each camera are the same vehicle; S5. According to the time when each camera collects images and the distance between the corresponding camera and the gate, determine whether the passing vehicle is leaving the warehouse and predict the time when the vehicle arrives at the gate, control the gate to open in advance, and the vehicle leaves the warehouse.

2. According to claim 1, a smart garage gate control method using image analysis technology is characterized by: The specific process of step S1 is as follows: S1-1. Establish a coordinate system with the center of the vehicle as the origin, the straight line where the center axis of the vehicle is located as the x-axis, the straight line perpendicular to the x-axis and passing through the origin as the y-axis, and the straight line perpendicular to the x-axis and y-axis and passing through the origin as the z-axis. The angle between the camera and the origin in the coordinate system is used as the shooting angle. S1-2, collect N1 images, and record the ath image as T a，b，θ , T a，b，θ Indicates that the a-th image is an image of the b-th vehicle model taken at a shooting angle of θ; N1 is a positive integer; a∈[1, N2], N2 is a positive integer; b∈[1, N3], N3 is a positive integer; θ∈[-2π, -2π]; T a，b，θ The intensity value of the red channel of the cth pixel in is recorded as T a，b，θ The intensity value of the green channel of the cth pixel in is recorded as T a，b，θ The intensity value of the blue channel of the cth pixel in is recorded as N4 is a positive integer; To T a，b，θ Perform grayscale processing to obtain the grayscale image of the a-th image, denoted as Tgary a，b，θ , Tgary a，b，θ The grayscale image representing the ath image is the grayscale image of the image of the bth vehicle model taken at a shooting angle of θ; The grayscale value of a pixel in a grayscale image is calculated according to the following formula: in, Represents Tgary a，b，θ The gray value of the cth pixel in ; α1, α2 and α3 are fixed parameters preset by the system; S1-3, the color of the vehicle in the ath image is the dth color, d∈[1,N5], N5 is a positive integer; About Tgary a，b，θ The gray value of each pixel in is screened to select Tgary a，b，θ The most frequent As the gray value corresponding to the dth color; Get the gray value corresponding to the dth color in the N1 images collected, and take the average gray value corresponding to the dth color in each image as the comprehensive gray value corresponding to the dth color, recorded as Igary d .

3. According to claim 2, a smart garage gate control method using image analysis technology is characterized in that: The specific process of step S2 is as follows: S2-1. Extract Tgary using image recognition technology a，b，θ The contour of the vehicle in the a-th image is obtained, and the contour point set of the vehicle in the grayscale image is recorded as the set S2-2. Establish a deep learning model and transform θ and As input, the b-th car model is taken as output to train the deep learning model.

4. According to claim 3, a smart garage gate control method using image analysis technology is characterized in that: The specific process of step S3 is as follows: S3-1, the time when the waiting vehicle arrives at the gate position is recorded as time t1; the waiting vehicle arriving at the gate position at time t1 is recorded as car t1 ; The image captured at time t1 is recorded as The shooting angle is right Perform grayscale processing to obtain a grayscale image, denoted as S3-2. Using optical character recognition technology Perform analysis and obtain car t1 The license plate number is recorded as S3-3. Extraction car t1 The outline of Combination Get car through deep learning model t1 The model of S3-4, through The intensity values ​​of the red, green, and blue channels of each pixel in The gray value of each pixel in The gray value of each pixel in is filtered to select The gray value that appears most often in the t1 The comprehensive gray value is recorded as like satisfy car t1 The color is the dth color; α4 is a fixed parameter preset by the system; like Dissatisfied car t1 The color of is not the d-th color; S3-5, record car t1 of and color and control the gate opening.

5. According to claim 4, a smart garage gate control method using image analysis technology is characterized in that: The specific process of step S4 is as follows: S4-1, each camera in the garage collects images of passing vehicles, and arranges the cameras from earliest to latest in the order of collection time; The time when the e-th camera captures the image of the passing vehicle is recorded as t2 e , e∈[1,N6], N6 is a positive integer; According to t2 e Arrange the cameras from the earliest to the latest moment in time; Filter out cameras that meet the following conditions: Among them, α5 and α6 are fixed parameters preset by the system; The time when the fth camera captures the image of the passing vehicle is recorded as t3 f , f∈[1,N7], N7 is a positive integer and N7≤N6; set t3 f The vehicle passing the fth camera at time is denoted as T3 f The image captured by the fth camera at time is recorded as The shooting angle is S4-2. Acquisition middle of and color, among which, express The license plate number; express Model of car; Will of and color, and car t1 of and color matching; like of and color are the same as car t1 of If the color is the same, the camera captures the image With car t1 It’s the same car; Filter out the collected cars t1 The cameras of the images are rearranged in the order of acquisition time from earliest to latest; The g-th camera captures the image to car t1 The moment of the image is recorded as t4 g , g∈[1,N8], N8 is a positive integer and N8≤N7.

6. The method for controlling a smart garage gate using image analysis technology according to claim 5 is characterized in that: The specific process of step S5 is as follows: S5-1. Let the distance between the gth camera and the gate be L. g ; If L g >L g+1 , then the passing vehicle leaves the warehouse; If L g ≤L g+1 , then the passing vehicles will not leave the warehouse; Among them, the N8+1th camera represents the camera at the gate and L N8+1 =0; S5-2. Predict the time when the vehicle arrives at the gate according to the following formula: in, Indicates car t1 Predicted time of arrival at the gate; S5-3, advance control of the gate When it is turned on, the vehicle leaves the warehouse.

7. A smart garage gate control system using image analysis technology, such as a smart garage gate control method using image analysis technology as claimed in any one of claims 1 to 6, characterized in that: The system includes an information collection module, an information processing module and an execution module; The information acquisition module is used to acquire the information required by the system; the information processing module is used to store, analyze and transmit the information of each module; and the execution module is used to execute the information of the information processing module.

8. The intelligent garage gate control system using image analysis technology according to claim 7 is characterized by: The information collection module includes a vehicle information unit and a garage information unit; The vehicle information unit is used to collect data information of the vehicle, and the garage information unit is used to collect data information of the camera in the garage.

9. The intelligent garage gate control system using image analysis technology according to claim 7 is characterized by: The information processing module includes an information storage unit, an information analysis unit and an information transmission unit; The information storage unit is used to store the information acquired by the information acquisition module; the information analysis unit is used to analyze the information acquired by the information acquisition module; and the information transmission unit is used for information transmission of each module in the system.

10. The intelligent garage gate control system using image analysis technology according to claim 7, characterized in that: The information display module includes a display unit and a gate control unit; The display unit is used to display the information of vehicles entering and exiting the garage, and the gate control unit is used to control the opening of the garage gate.

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