CN117994994B

CN117994994B - Tunnel construction vehicle passing and traffic red-green lamp tube control method

Info

Publication number: CN117994994B
Application number: CN202410396857.3A
Authority: CN
Inventors: 陈大伟; 杨晋文; 周宗华; 王武现; 邵长青; 宋青波; 左学峰; 钱富林; 齐少华; 张宏伟
Original assignee: China Railway 16th Bureau Group Co Ltd
Current assignee: China Railway 16th Bureau Group Co Ltd
Priority date: 2024-04-03
Filing date: 2024-04-03
Publication date: 2024-06-18
Anticipated expiration: 2044-04-03
Also published as: CN117994994A

Abstract

The application relates to the technical field of image data processing, and provides a tunnel construction vehicle passing and traffic red-green lamp tube control method, which comprises the following steps: acquiring a tunnel vehicle monitoring gray level image, acquiring a tunnel vehicle monitoring local characteristic region according to the tunnel vehicle monitoring gray level image, and acquiring a tunnel light interference difference coefficient according to the tunnel vehicle monitoring local characteristic region; acquiring a tunnel monitoring interference distribution characteristic value according to the tunnel lamplight interference difference coefficient; calculating a tunnel vehicle monitoring interference characteristic value according to the tunnel monitoring interference distribution characteristic value; and calculating a tunnel interference filtering window coefficient according to the tunnel vehicle monitoring interference characteristic value, carrying out noise reduction processing on the tunnel vehicle monitoring gray level image according to the tunnel interference filtering window coefficient, acquiring vehicle information data according to the noise-reduced tunnel vehicle monitoring image, and realizing tunnel traffic red-green lamp control according to the vehicle information data. According to the application, the accuracy of controlling the red and green lamp tubes in tunnel traffic is improved by acquiring accurate vehicle information data.

Description

Tunnel construction vehicle passing and traffic red-green lamp tube control method

Technical Field

The application relates to the technical field of image data processing, in particular to a traffic light control method for a tunnel construction vehicle.

Background

The expressway is used as an important traffic construction building facility, plays an important role in shortening the running distance, improving the transportation capacity and the like, and has the characteristics of large traffic flow and high running speed in traffic state, but the main working procedures of tunnel construction at present are blasting excavation, slag discharge, primary support, inverted arch, secondary lining and the like, and due to the influence of tunnel construction progress factors, a project tunnel channel is long and narrow, the space in a tunnel is usually narrow, and safety risks caused by traffic jam and dislocation of vehicles and personnel exist. Meanwhile, vision blind areas exist in import and export vehicles due to turning or other reasons, so that the method is not beneficial to large-scale vehicle dispatching and transportation.

At present, a traffic signal lamp in a tunnel is mainly controlled and regulated by a construction management department, but when complex construction sections and more intersections are met, the existing signal lamp control mode is not flexible enough, the stay time of the signal lamp is not reasonable enough, so that construction vehicles are frequently jammed, even the phenomenon of unconscious regulation and violation of drivers is caused, the vehicle scheduling efficiency is poor, monitoring analysis of vehicles in the tunnel mainly relies on collecting image data of the vehicles in the tunnel, but the influence of light change and vehicle light change in the tunnel is low in quality of collected images, the image data is processed through traditional image noise reduction algorithms, such as self-adaptive filtering, wiener filtering and the like, and the accuracy of extracting information of the vehicles in the tunnel according to the processed images is low, so that the traffic in the tunnel and the management and control of the traffic signal lamp are affected.

Disclosure of Invention

The application provides a method for controlling traffic vehicles and traffic red and green lamps in tunnel construction, which aims to solve the problem of low accuracy of tunnel traffic control, and adopts the following technical scheme:

in a first aspect, an embodiment of the present application provides a method for controlling traffic and traffic lights of a tunnel construction vehicle, the method comprising the steps of:

The application scene is a tunnel construction work area which comprises one or more than two parallel tunnel positive holes and more than one inclined shaft obliquely crossing the tunnel positive holes; the tunnel positive hole is formed by face areas at two ends, and an intersection which is in cross connection with the inclined shaft is arranged in the face areas; a vehicle velocimeter is arranged in front of each intersection, and traffic prompt traffic lights and RFID base station sensors are arranged in all driving directions of each intersection;

Acquiring a tunnel vehicle monitoring image through a vehicle velocimeter, converting the tunnel vehicle monitoring image into a gray image, and taking the converted result image as the tunnel vehicle monitoring gray image;

Acquiring a tunnel vehicle monitoring local feature area according to the tunnel vehicle monitoring gray level image, and determining a neighbor feature matrix of each pixel point based on the tunnel vehicle monitoring local feature area; acquiring a vehicle monitoring local linear feature pair of each pixel point according to a neighbor feature matrix of each pixel point in the tunnel vehicle monitoring local feature area; calculating tunnel light interference difference coefficients of each pixel point according to the vehicle monitoring local linear characteristic pairs of each pixel point in the tunnel vehicle monitoring local characteristic areas; acquiring a tunnel vehicle monitoring interference characteristic value according to the tunnel light interference difference coefficient of each pixel point in the tunnel vehicle monitoring local characteristic region;

Acquiring a tunnel interference filtering window coefficient of the tunnel vehicle monitoring gray level image according to the tunnel vehicle monitoring interference characteristic value; and acquiring a noise-reduced tunnel vehicle monitoring gray image according to the tunnel interference filtering window coefficient, and realizing tunnel traffic red and green light control based on the noise-reduced tunnel vehicle monitoring gray image and vehicle label information.

Preferably, the method for acquiring the local feature area of the tunnel vehicle monitoring according to the gray level image of the tunnel vehicle monitoring, and determining the neighbor feature matrix of each pixel point based on the local feature area of the tunnel vehicle monitoring comprises the following steps:

Obtaining clustering results of all pixel points in the tunnel vehicle monitoring gray level image by using a clustering algorithm, and taking a region formed by the pixel points in each cluster in the clustering results of all pixel points of the tunnel vehicle monitoring gray level image as a tunnel vehicle monitoring local characteristic region;

and constructing a neighbor feature matrix with a preset parameter by taking each pixel point in the local feature area monitored by the tunnel vehicle as a center, and taking the pixel point corresponding to all elements in the neighbor feature matrix of each pixel point in the local feature area monitored by the tunnel vehicle as the neighbor feature pixel point of each pixel point.

Preferably, the method for obtaining the vehicle monitoring local linear feature pair of each pixel point according to the neighboring feature matrix of each pixel point in the tunnel vehicle monitoring local feature area includes:

For each pixel point in the tunnel vehicle monitoring local feature area, respectively taking a neighboring feature matrix of the pixel point and a transposed matrix of the neighboring feature matrix as input, respectively acquiring variance expansion factors of the neighboring feature matrix and the transposed matrix of the neighboring feature matrix by adopting a variance expansion factor algorithm, and taking a symbiotic pair formed by the variance expansion factors as a vehicle monitoring local linear feature pair of each pixel point.

Preferably, the method for calculating the tunnel light interference difference coefficient of each pixel point according to the vehicle monitoring local linear characteristic pair of each pixel point in the tunnel vehicle monitoring local characteristic region comprises the following steps:

for each pixel point in the tunnel vehicle monitoring local feature area, taking a measurement result of similarity between a neighbor feature matrix of the pixel point and a neighbor feature matrix of each neighbor feature pixel point of the pixel point as a molecule, taking a sum of a dot product ratio of a vehicle monitoring local linear feature pair of each neighbor feature pixel point of the pixel point and 0.01 as a denominator, calculating a mean value of accumulation results of the ratio of the molecule and the denominator on all neighbor feature pixel points of the pixel point, and taking a product of the mean value and a variation coefficient of all elements in the neighbor feature matrix of the pixel point as a tunnel light interference difference coefficient of the pixel point.

Preferably, the method for obtaining the tunnel vehicle monitoring interference characteristic value according to the tunnel light interference difference coefficient of each pixel point in the tunnel vehicle monitoring local characteristic region comprises the following steps:

Classifying tunnel lamplight interference difference coefficients of all pixel points in each tunnel vehicle monitoring local characteristic region, and calculating tunnel detection interference distribution characteristic values corresponding to each group of classified data;

For any two groups of classified data in a local feature area of tunnel vehicle monitoring, taking one group of classified data as first classified data, the other group of classified data as second classified data, taking the product of the number of data in the first classified data and the average value of all data in the first group of classified data as first comparison data, taking the product of the number of data in the second classified data and the average value of all data in the second group of classified data as second comparison data, taking the absolute value of the difference between the first comparison data and the second comparison data as a molecule, acquiring the number of data in each group of classified data in the local feature area of tunnel vehicle monitoring, taking the maximum value in all the numbers as a denominator, and taking the ratio of the molecule to the denominator as a tunnel vehicle feature weakening comparison coefficient between any two groups of classified data;

And regarding each tunnel vehicle monitoring local characteristic region in the tunnel vehicle monitoring gray level image, taking a tunnel vehicle characteristic weakening contrast coefficient between any two groups of classified data in the tunnel vehicle monitoring local characteristic region as a numerator, taking the sum of the absolute value of the difference between tunnel detection interference distribution characteristic values of any two groups of classified data in the tunnel vehicle monitoring local characteristic region and 0.01 as a denominator, and taking the average value of the accumulation result of the ratio of the numerator and the denominator on the tunnel vehicle monitoring local characteristic region as the tunnel vehicle monitoring interference characteristic value of the tunnel vehicle monitoring local characteristic region.

Preferably, the method for classifying the tunnel light interference difference coefficients of all pixel points in the monitoring local feature area of each tunnel vehicle and calculating the tunnel detection interference distribution feature value corresponding to each group of data comprises the following steps:

taking tunnel light interference difference coefficients of all pixel points in a local feature area monitored by the tunnel vehicle as input, obtaining classification results of the tunnel light interference difference coefficients of all pixel points by clustering, and taking the pixel point at the leftmost side in the horizontal direction in the local feature area monitored by the tunnel vehicle as a starting point to make a horizontal right ray;

And for each group of data of tunnel light interference difference coefficients of all pixel points in the local feature area monitored by the tunnel vehicle, mapping the pixel points corresponding to all elements in each group of data onto the ray in the vertical direction, and taking the length of a straight line obtained by fitting all the mapped data points as the tunnel monitoring interference distribution feature value of each group of data.

Preferably, the specific method for obtaining the tunnel interference filter window coefficient of the tunnel vehicle monitoring gray level image according to the tunnel vehicle monitoring interference characteristic value comprises the following steps:

In the method, in the process of the invention, A tunnel interference filter window coefficient representing a tunnel vehicle monitoring gray level image; /(I)Representing the/>, in a tunnel vehicle monitoring gray imageTunnel vehicle monitoring interference characteristic values of the local tunnel detection characteristic areas; /(I)Representing the/>, in a tunnel vehicle monitoring gray imageDetecting the number of pixel points in the local feature area by the tunnels; /(I)And/>Is a constant coefficient; /(I)Representing the number of pixel points in the monitoring gray level image of the tunnel vehicle,/>The method comprises the steps of representing the sum of tunnel vehicle monitoring interference characteristic values of all tunnel detection local characteristic areas in a tunnel vehicle monitoring gray level image; /(I)The number of the tunnel detection local characteristic areas in the tunnel vehicle monitoring gray level image is represented; /(I)Representing a round-up function.

Preferably, the method for obtaining the noise-reduced tunnel vehicle monitoring gray level image according to the tunnel interference filtering window coefficient and realizing the tunnel traffic red-green lamp control based on the noise-reduced tunnel vehicle monitoring gray level image and the vehicle label information comprises the following steps:

Taking a tunnel vehicle monitoring gray level image and a corresponding tunnel interference filtering window coefficient as inputs, adopting a BM3D image denoising algorithm to obtain a result image after the tunnel vehicle monitoring gray level image is denoised, taking tunnel interference filtering window parameters as parameters for dividing the size of image blocks in the BM3D image denoising algorithm, and taking the result image as the tunnel vehicle monitoring gray level image after the noise is reduced;

Sending the noise-reduced tunnel vehicle monitoring image to a system management platform for image signal processing to obtain a clear image of a tunnel vehicle target; sensing a vehicle tag through an RFID base station sensor, and binding vehicle information; meanwhile, the vehicle operation data is transmitted to a system management platform through a license plate recognition system, and the traffic light management and control step through the system management platform comprises the following steps:

S1, taking the position of each vehicle monitored in a tunnel as a node, and taking the distance between each vehicle and the previous vehicle as a section to construct a tunnel construction traffic chain;

S2, setting a threshold value of the number of vehicles corresponding to the tunnel according to the road section distance between any vehicle in the tunnel construction section and the previous road junction, the number of vehicles and the preset minimum distance between vehicles;

S3, acquiring light intensity data of vehicle lamps in the tunnel construction section through a video detection assembly, and compensating a vehicle quantity threshold value of the tunnel according to the light intensity data to obtain a reference traffic chain corresponding to the tunnel construction section;

s4, calculating a difference value between the number of vehicles in the tunnel construction section and a threshold value of the number of vehicles of the reference traffic chain, determining whether congestion occurs in the tunnel according to the difference value, and controlling a traffic light according to a judging result of the congestion condition to realize real-time management and control of tunnel construction traffic operation;

S5, voice audible and visual alarms are installed at each construction point in the tunnel positive hole and the inclined shaft, if the fact that the same vehicle passes through 2 or more speed measuring points in the running process is monitored, overspeed conditions continuously occur, voice broadcasting and audible and visual reminding are triggered, and personnel on the construction surface are reminded to avoid urgently.

In a second aspect, an embodiment of the present application further provides a computer device, where the computer device includes a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of any one of the methods described above when the processor executes the computer program.

The beneficial effects of the application are as follows: the engineering vehicles entering the tunnel are controlled in an indexing way by a tunnel construction vehicle passing and traffic red-green lamp tube control method, so that the idle running of the vehicles is reduced, the resource and time cost is saved, the control is simple and convenient, and the functions are perfect; by the application of the platform intelligent technology, the vehicle velocimeter, the vehicle base station and the traffic light are linked, so that manual intervention is reduced; after the tunnel is closed or before blasting in the tunnel, traffic light colors can be uniformly set in the background, so that vehicles and personnel are prevented from entering the tunnel, and the vehicle dispatching passing efficiency and the accident emergency handling efficiency are effectively improved; the method has the advantages that unstable characteristics of light changes in the tunnel are considered, image denoising processing parameters are adjusted in a self-adaptive mode, and accuracy of controlling the red and green light tubes of tunnel traffic by utilizing the collected vehicle images in the tunnel is improved.

Drawings

In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.

FIG. 1 is a schematic flow chart of a method for controlling traffic and traffic lights of a tunnel construction vehicle according to an embodiment of the present application;

FIG. 2 is a schematic layout diagram of the sensors of the base station and the traffic lights for prompt and RFID installed in all directions at A, B, C, D and E intersections;

FIG. 3 is a schematic diagram of a vehicle E entering the area of the face A according to an embodiment of the present invention, if no vehicle is present in the vehicle exit direction of the face A, B, the vehicle E normally runs;

FIG. 4 is a schematic view of the traffic rules of the E-car entering the area of the A face if the A face has a car exiting the E-car;

FIG. 5 is a schematic view of the traffic rules of E vehicles entering the area of the face A if the face B has vehicles exiting;

FIG. 6 is a schematic illustration of the normal passage of E-vehicles into the B face area if A, B face exit direction is empty;

FIG. 7 is a schematic view of the traffic rules of the E-car entering the B-face area if the A-face has a car exiting the E-car;

FIG. 8 is a schematic view of the traffic rules of the E-car entering the B-face area if the B-face has a car coming out;

FIG. 9 is a schematic illustration of the normal passage of E-vehicles into the C face area if C, D face exit direction is empty;

FIG. 10 is a schematic view of the traffic rules of E vehicles entering the area of the face C if the face C has vehicles exiting;

FIG. 11 is a schematic view of the traffic rules of the E-car entering the C-face area if the D-face has a car coming out;

FIG. 12 is a schematic illustration of the normal passage of E-vehicles into the D face area if C, D face exit direction is empty;

FIG. 13 is a schematic view of the traffic rules of E vehicles entering the area of the face D if the face C has vehicles exiting;

FIG. 14 is a schematic view of the traffic rules of E-cars entering the D-face area if the D-face has cars coming out;

FIG. 15 is a schematic view of traffic rules of the face A exiting the car A and the car E having arrived between the intersection E and the intersection B in the special situation;

FIG. 16 is a schematic view of traffic rules for the face A to exit the car A, and the car E has arrived between the intersection B and the intersection A in the special situation;

FIG. 17 is a schematic view of the traffic rules of the face B exiting the car B and the car E having arrived between the intersection E and the intersection B in the special situation;

FIG. 18 is a schematic view of traffic rules for the face B exiting the car B, the car A having arrived between the intersection B and the intersection A in the event of a special situation;

FIG. 19 is a schematic view of the traffic rules of the face C exiting the car C and the car E having arrived between the intersection E and the intersection D in the special situation;

FIG. 20 is a schematic view of a traffic rule that a C tunnel face exits a C vehicle and an E vehicle has arrived between a D intersection and a C intersection under special conditions;

FIG. 21 is a schematic view of the traffic rules of the face D exiting the vehicle D, the vehicle E having arrived between the intersection E and the intersection D in the event of a special situation;

FIG. 22 is a schematic view of a traffic rule that a D tunnel face exits a D car and a C car has arrived between a C intersection and a D intersection in a special situation;

fig. 23 is a block diagram of a computer device.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Referring to fig. 1, a flowchart of a method for controlling traffic and traffic lights of a tunnel construction vehicle according to an embodiment of the application is shown, the method includes the following steps:

and S001, acquiring a tunnel vehicle monitoring gray level image.

The application scene is a tunnel construction work area which comprises one or more than two parallel tunnel positive holes and more than one inclined shaft obliquely crossing the tunnel positive holes; the tunnel positive hole is formed by face areas at two ends, and an intersection which is in cross connection with the inclined shaft is arranged in the face areas; a vehicle velocimeter is arranged at the position of the front 100 meters of each intersection, and traffic prompt traffic lights and RFID base station sensors are arranged in all driving directions of each intersection; the vehicles enter the tunnel construction work area from the tunnel entrance to carry out construction operation, the vehicles which go out are arranged at each intersection in the work area to pass preferentially, the vehicles which go in wait for passing pass rules, and the vehicles are driven out from the tunnel entrance after the construction operation is finished.

The vehicle velocimeter includes: the system comprises a video detection assembly, a Doppler radar (speed measuring radar), a snapshot host, a license plate recognition system and a system management platform, wherein speed monitoring and video image monitoring are implemented on a running vehicle in a tunnel.

Further, the speed of the mobile vehicle is measured through a Doppler radar (speed measuring radar) in the vehicle velocimeter, the traffic image of the tunnel vehicle is collected through a video detection assembly (camera) and a snapshot host, the image of the tunnel vehicle is used as a tunnel vehicle monitoring image, the tunnel vehicle monitoring image is converted into a gray image, and the gray image of the tunnel vehicle monitoring image is used as a tunnel vehicle monitoring gray image.

Thus, a tunnel vehicle monitoring gray scale map is obtained.

Step S002, obtaining a tunnel vehicle monitoring local characteristic region according to the tunnel vehicle monitoring gray level image, and obtaining a tunnel light interference difference coefficient according to the tunnel vehicle monitoring local characteristic region; and obtaining a tunnel monitoring interference distribution characteristic value according to the tunnel lamplight interference difference coefficient.

Because the light contrast is great in the tunnel and outside the tunnel, in order to prevent that the driver from producing black hole effect and guaranteeing the safety of traveling of vehicle, operating company often can control the opening and closing of tunnel lamps and lanterns through tunnel PLC according to weather and time, reaches the purpose of adjustment tunnel light, but the light adjustment in the tunnel can be the monitoring environment and produces different background effects, the circumstances that background luminance is higher, background luminance is lower, local bright spot appears in the background and background ambient luminance is even probably appears, consequently carry out the light change of in-process tunnel of image acquisition in the tunnel can lead to the monitoring image quality who gathers lower.

Furthermore, as the vehicle enters the tunnel and must turn on the light, the headlight, the turn signal light and the brake light of the vehicle interfere with the monitoring of the tunnel environment in the tunnel environment, and the light moves together with the vehicle in the process of running in the tunnel, the interference light source appears in the monitoring area, so that the light source in the monitoring area and the vehicle serve as a prospect together, and the accuracy of the monitoring of the vehicle in the tunnel is reduced; meanwhile, the image in the shot monitoring area can be interfered due to equipment reasons in the tunnel, so that the condition that the monitoring image is blurred and the influence degree of the environmental interference in the monitoring image is large is caused.

Further, if the light change in the tunnel and the light started by the vehicle interfere in the monitoring area in the tunnel, shadows may occur in the target vehicle area in the light irradiation angle and light intensity change monitoring image, and the vehicle area image is blurred, namely, different degrees of textures are weakened on the basis of complex textures of the vehicle, so that the image characteristics of the target vehicle are close to the background characteristics, and therefore, the tunnel light interference difference coefficient is calculated according to the local fuzzy brightness difference caused by the texture weakening and light interference in the tunnel vehicle monitoring image. Specifically, the input is a tunnel vehicle monitoring gray level map, a STING grid clustering algorithm is adopted to obtain a clustering result of the tunnel vehicle monitoring image, and because the tunnel vehicle monitoring gray level map may have environmental interference, the clustering result of the image reflects a dividing result of a region with similar gray level characteristics, the STING grid clustering algorithm is a known technology, and a specific calculation process is not repeated.

Further, taking an area formed by corresponding pixel points of each cluster in the image in the clustering result of the tunnel vehicle monitoring gray level image as a tunnel vehicle monitoring local feature area, taking one tunnel vehicle monitoring local feature area as an example, and taking each pixel point in the tunnel vehicle monitoring local feature area as a center to constructA neighbor feature matrix of size, wherein/>Taking a checked value 11, and taking the pixel point corresponding to all elements in the neighbor feature of each pixel point in the tunnel vehicle monitoring local feature area as the neighbor feature pixel point of each pixel point; the method comprises the steps of taking a neighbor feature matrix of each pixel point and a transposed matrix of the neighbor feature matrix as input respectively, adopting a variance expansion factor algorithm to obtain variance expansion factors of the neighbor feature matrix and the transposed matrix of the neighbor feature matrix respectively, taking a symbiotic pair formed by the variance expansion factors as a vehicle monitoring local linear feature pair of each pixel point, reflecting transverse and longitudinal collinearity features in a neighbor area of the pixel point through the variance expansion factors, namely, the larger the influence of light irradiation in the area is, the more obvious the transverse and longitudinal collinearity features in the area are, the more the influence degree on vehicle monitoring is likely to be, the variance expansion factor algorithm is a known technology, and detailed calculation process is omitted.

Further, according to the vehicle monitoring local linear characteristic pair of each pixel point in the tunnel vehicle monitoring local characteristic area, the tunnel light interference difference coefficient is calculated, and a specific calculation formula is as follows:

In the method, in the process of the invention, Representing the/>, in a monitored local feature region of a tunnel vehicleTunnel light interference difference coefficients of the pixel points; /(I)Representing the/>, in a monitored local feature region of a tunnel vehicleNeighbor feature matrix of each pixel point,/>Representing the/>, in a monitored local feature region of a tunnel vehicleFirst pixel/>A neighboring feature matrix corresponding to each neighboring feature pixel point,Representation/>And/>SAD values in between; /(I)Representing the/>, in a monitored local feature region of a tunnel vehicleVehicle monitoring local linear characteristic pair of each pixel point,/>Representing the/>, in a monitored local feature region of a tunnel vehicleFirst pixel/>Vehicle monitoring local linear characteristic pair corresponding to each neighboring characteristic pixel point,/>Representation/>And/>Dot product ratio between; Representing the/>, in a monitored local feature region of a tunnel vehicle Variation coefficients of all elements in the neighbor feature matrix of each pixel point; /(I)Representing the/>, in a monitored local feature region of a tunnel vehicleThe number of elements in the neighbor feature matrix of the individual pixel points.

If the tunnel vehicle monitors the first local characteristic regionThe brightness variation in the local area of each pixel point is larger, and the/>The local characteristic difference between each pixel point and other pixel points in the local area is larger, and the calculated/>AndThe greater the value of (2); while tunnel vehicle monitors the/>, in local feature regionThe collinearity characteristic between each pixel point and other pixel points in the local area is approximate, namely the calculated/>The smaller the value of (c) is, the more/>, is represented in the monitored local feature region of the tunnel vehicleThe characteristics of the transverse and longitudinal linear changes in the local area of each pixel point are close, and the calculated tunnel vehicle monitoring local characteristic area is the/>Tunnel light interference difference coefficient of each pixel point represents the/>, in the local feature area monitored by the tunnel vehicleThe greater the likelihood of weakening the texture caused by light interference in a localized area of individual pixels.

Further, tunnel light interference difference coefficients of all pixel points in the local feature area monitored by the tunnel vehicle are used as input, classification results of the tunnel light interference difference coefficients of all pixel points are obtained by means of aggregation hierarchical clustering, the aggregation hierarchical clustering algorithm is a known technology, and detailed calculation processes are not repeated. Taking the leftmost pixel point in the horizontal direction of the local feature area monitored by the tunnel vehicle as a starting point to make a horizontal right ray; for each group of data of tunnel light interference difference coefficients of all pixel points in a tunnel vehicle monitoring local feature area, mapping all pixel points in each group of data onto the ray in the vertical direction, taking the length of a straight line formed by all mapped data points as a tunnel monitoring interference distribution feature value of each group of data, and calculating the tunnel vehicle monitoring interference feature value according to the distribution difference and the feature difference between different groups in the classification result of the tunnel light interference difference coefficients of all pixel points in the tunnel vehicle monitoring local feature area, wherein a specific calculation formula is as follows:

，

In the method, in the process of the invention, Represents the/>Tunnel vehicle monitoring interference characteristic values of the local characteristic areas monitored by the tunnel vehicles; /(I)AndRespectively represent the/>The first/>, in monitoring local feature areas by individual tunnel vehiclesGroup and/>Average of all data in group data,/>And/>Respectively represent the/>The first/>, in monitoring local feature areas by individual tunnel vehiclesGroup and/>Number of data in group classification data,/>Represents the/>A set of the number of data in all the component class data in the local feature area is monitored by the individual tunnel vehicles,/>Representation/>Maximum value of/>Represents the/>The first/>, in monitoring local feature areas by individual tunnel vehiclesGroup and/>The tunnel vehicle characteristics between the group data weaken the contrast coefficient; /(I)And/>Respectively represent the/>The first/>, in monitoring local feature areas by individual tunnel vehiclesGroup and/>Tunnel monitoring interference distribution characteristic values of the component data; /(I)Represents the/>The individual tunnel vehicles monitor the number of divisions of the classification data in the local feature region.

If at firstIf the texture weakening condition caused by the influence of light in the local feature area of each tunnel vehicle monitoring is serious, the larger the difference value of the tunnel light interference difference coefficients of different pixel points in the local feature area of the tunnel vehicle monitoring is, namely the calculated/>The greater the value of (2); at the same time/>The difference of the texture weakness degree of different pixel points in the local feature area monitored by each tunnel vehicle is larger, but the distribution is discrete, and the/>, obtained by calculation, is calculatedThe smaller the value of (2), the calculated/>Tunnel vehicle monitoring interference characteristic value/>, of each tunnel vehicle monitoring local characteristic regionThe larger the value of (2) is, the more/>The detail texture weakening features of the light-affected images in the local feature areas monitored by the tunnel vehicles are obvious.

Thus, the monitoring interference characteristic value of the tunnel vehicle is obtained.

Step S003, calculating a tunnel vehicle monitoring interference characteristic value according to the tunnel monitoring interference distribution characteristic value; and calculating a tunnel interference filtering window coefficient according to the tunnel vehicle monitoring interference characteristic value, and carrying out noise reduction treatment on the tunnel vehicle monitoring gray level image according to the tunnel interference filtering window coefficient.

According to the detail texture weakening characteristics of different areas in the tunnel vehicle monitoring gray level image, tunnel interference filtering window coefficients are calculated, window coefficients of noise reduction processing of the tunnel vehicle monitoring gray level image, which are influenced by detail texture weakening caused by light or other environmental factors in a tunnel, are selected according to the tunnel interference filtering window coefficients, and a specific calculation formula is as follows:

In the method, in the process of the invention, A tunnel interference filter window coefficient representing a tunnel vehicle monitoring gray level image; /(I)Representing the/>, in a tunnel vehicle monitoring gray imageTunnel vehicle monitoring interference characteristic values of the local tunnel detection characteristic areas; /(I)Representing the/>, in a tunnel vehicle monitoring gray imageDetecting the number of pixel points in the local feature area by the tunnels; /(I)And/>The constant coefficient is a constant coefficient, and the values are 3 and 20 respectively; /(I)Representing the number of pixel points in the monitoring gray level image of the tunnel vehicle,/>The method comprises the steps of representing the sum of tunnel vehicle monitoring interference characteristic values of all tunnel detection local characteristic areas in a tunnel vehicle monitoring gray level image; /(I)The number of the tunnel detection local characteristic areas in the tunnel vehicle monitoring gray level image is represented; /(I)Representing a round-up function.

If the interference influence degree of local detail features in the tunnel vehicle monitoring gray level image is large due to the influence of tunnel light or environmental factors on different tunnel detection local feature regions in the tunnel vehicle monitoring gray level image, calculating to obtainThe larger the value of (2) >, theThe smaller the value of (2), the tunnel interference filter window coefficient/>, of the tunnel vehicle monitoring gray level image is calculatedThe smaller the value of (c) is, the more the local interference characteristics should be considered when the filtering noise reduction is carried out on the monitoring gray level image of the tunnel vehicle.

Further, the tunnel vehicle monitoring gray level image and the corresponding tunnel interference filtering window coefficient are processedThe method comprises the steps that as input, a BM3D image denoising algorithm is adopted to obtain a result image of a tunnel vehicle after noise reduction of a monitoring gray level image, wherein tunnel interference filtering window parameters are used as parameters for dividing the size of an image block in the BM3D image denoising algorithm, the BM3D image denoising algorithm is a known technology, and detailed calculation process is not repeated; and taking the result image as a noise-reduced tunnel vehicle monitoring gray scale image.

So far, the monitoring gray level map of the tunnel vehicle after noise reduction is obtained.

And S004, acquiring vehicle information data according to the noise-reduced tunnel vehicle monitoring image, and realizing tunnel traffic red-green lamp control according to the vehicle information data.

Sending the noise-reduced tunnel vehicle monitoring gray level image to a system management platform (industrial personal computer) for image signal processing to obtain a clear image of a tunnel vehicle target; sensing a vehicle tag through the RFID base station sensor, and binding vehicle information; meanwhile, the vehicle operation data is transmitted to a system management platform through a license plate recognition system, and the traffic light management and control step through the system management platform comprises the following steps:

S1, acquiring the position of each vehicle in a tunnel construction section through a system management platform, taking the position of each vehicle monitored in the tunnel construction section as a node, and taking the distance between each vehicle and the previous vehicle as a section to construct a tunnel construction traffic chain.

S2, setting a threshold value of the number of vehicles corresponding to the tunnel according to the road section distance between any vehicle in the tunnel construction section and the previous road junction, the number of vehicles and the preset minimum distance between vehicles.

S3, acquiring light intensity data of vehicle lamps in the tunnel construction section through a video detection assembly, and compensating the threshold value of the number of vehicles in the tunnel according to the light intensity data to obtain a reference traffic chain corresponding to the tunnel construction section.

S4, calculating a difference value between the number of vehicles in the tunnel construction section and a threshold value of the number of vehicles of the reference traffic chain, determining whether congestion occurs in the tunnel according to the difference value, and controlling the traffic light according to a judging result of the congestion condition to realize real-time management and control of tunnel construction traffic operation.

Further, in step S4, whether congestion occurs in the tunnel is determined according to the difference value, and a specific process of controlling the traffic light to implement real-time control of the tunnel construction traffic operation according to the determination result of the congestion condition includes:

S41, determining the number of vehicles in each tunnel positive hole and each inclined shaft according to the number of segments in the traffic chain corresponding to the tunnel positive hole and the inclined shaft.

S42, presetting a deviation threshold value between the number of vehicles in the tunnel positive hole and the inclined shaft and a reference traffic chain according to construction requirements.

S43, calculating the difference value between the real-time vehicle quantity in the tunnel positive tunnel and the inclined shaft and the vehicle quantity threshold value corresponding to the tunnel construction section.

When the difference value is smaller than a preset deviation threshold value, the phenomenon that the tunnel positive hole and the inclined shaft are not jammed is considered; and when the difference value is larger than a preset deviation threshold value, the congestion risk in the tunnel positive hole and the inclined shaft is considered to be increased, traffic light linkage is controlled, and the congestion risk is reported.

Further, on the basis of vehicle speed measurement, a voice reminding function is added, so that casualties are effectively prevented. According to the embodiment, the speed measuring radar and the snapshot equipment are installed in a main speed limiting area (climbing position, intersection of a positive hole and an inclined shaft and a meeting area) in the hole, the voice audible and visual alarm is installed in a construction area, and the vehicle in the hole is snapped and reminded to travel without overspeed, so that people avoid the vehicle. And the overspeed vehicle information can also be uploaded to a system, an Excel table is generated after statistical analysis, and the Excel table is exported and archived to be used as the basis for processing the illegal vehicles.

For example, according to the construction requirements: the speed limit running of vehicles passing through the hole is not more than 8Km/h, the speed of vehicles entering the hole is not more than 15Km/h, the climbing of vehicles leaving the hole is not more than 20Km/h, the speed of vehicles meeting is not more than 5Km/h, the high-grade and neutral-grade running is strictly forbidden, so that the out-of-grade and brake failure can be prevented, the overspeed vehicle is illegal, overspeed snapshot is carried out, overspeed vehicle reminding and recording can be checked on a system management platform, and the functions of reports and the like can be derived.

Further, the specific method for setting a traffic rule that the vehicles coming out preferentially pass through each intersection in the work area and the vehicles coming in wait to pass through according to the step S001 includes:

As shown in fig. 2, the tunnel construction work area comprises an A face area, a B face area, a C face area, a D face area and an E hole area, wherein an A road port which is in cross connection with an inclined shaft is arranged in the A face area, a B road port which is in cross connection with the inclined shaft is arranged in the B face area, a C road port which is in cross connection with the inclined shaft is arranged in the C face area, a D road port which is in cross connection with the inclined shaft is arranged in the D face area, and an E road port which is in cross connection with the inclined shaft is arranged in the E hole area; the method for planning the driving scheme by using the basic passing principle of first-out and last-in comprises the following steps of installing prompting traffic lights and RFID base station sensors in all driving directions of an intersection A, an intersection B, an intersection C, an intersection D and an intersection E respectively, and comprises the following steps:

The passing rule that the vehicles E at the E hole enter the face A is that; the passing rule that the vehicles E at the opening E enter the face B; the passing rule that the vehicles E at the E hole enter the face C; the passing rule that the vehicles E at the E hole enter the face D; the traffic rule of the vehicle A on the face A for exiting the vehicle; the passing rule of the vehicle B coming out of the face B; the passing rule of the vehicle C on the face C for the vehicle to go out is that; and D, the passing rule of the vehicles D on the face of the face D for exiting.

The passing rule of the vehicles E at the E tunnel portal entering the A tunnel face comprises the following steps:

(1) When the E vehicle enters the area of the face A, if no vehicle exists in the vehicle outlet directions of the face A and the face B, the E vehicle normally passes through: through the left turn at the E intersection, straight through the B intersection, through the left turn at the A intersection, and to the A face as shown in figure 3.

(2) If the face A has a vehicle A to go out, the left turn of the intersection E is red: and E vehicles are prohibited from turning left, the E vehicles wait in situ, and after A vehicles pass through an E intersection, the E vehicles can normally pass through the A tunnel face, as shown in fig. 4.

(3) If the B face has a vehicle B to go out, the left turn of the E intersection is red: and E vehicles are prohibited from turning left, the E vehicles wait in situ, and after B vehicles pass through an E intersection, the E vehicles can normally pass through the A tunnel face, as shown in fig. 5.

The passing rule of the vehicles E at the E hole entering the B face comprises the following steps:

(1) When the E vehicle enters the B tunnel face area, if no vehicle exists in the vehicle outlet directions of the A tunnel face and the B tunnel face, the E vehicle normally passes through: left turn through the E intersection, left turn through the B intersection, and reach the A face as shown in FIG. 6.

(2) If the face A has a vehicle A to go out, the left turn of the intersection E is red: e car waits, after A car passes E crossing, E car side can normally pass to B face, as shown in FIG. 7.

(3) If the B face has a vehicle B to go out, the left turn of the E intersection is red: the E vehicle is prohibited from turning left, the E vehicle waits in place, and after the B vehicle passes through the E intersection, the E vehicle can normally pass through the B tunnel face, as shown in fig. 8.

The passing rule of the vehicles E at the E hole entering the C tunnel face comprises the following steps:

(1) When the E vehicle enters the C tunnel face area, if no vehicle exists in the vehicle outlet direction of the C tunnel face and the D tunnel face, the E vehicle normally passes through: straight through the E intersection, left through the D intersection, right through the C intersection, and reach the C tunnel face as shown in FIG. 9.

(2) If the C tunnel face has a vehicle C to go out, the E intersection directly acts as a red light: e car waits for, after the C car passes E crossing, E car side can normally pass to C face, as shown in FIG. 10.

(3) If the D tunnel face has a vehicle D to go out, the E intersection directly acts as a red light: e car waits, after D car passes E crossing, E car side can normally pass to C face, as shown in FIG. 11.

The passing rule of the vehicles E at the E hole entering the D tunnel face comprises the following steps:

(1) When the E vehicle enters the D tunnel face area, if no vehicle exists in the vehicle outlet direction of the C tunnel face and the D tunnel face, the E vehicle normally passes through: straight through the E intersection, right turn through the D intersection and reach the D face as shown in FIG. 12.

(2) If the C tunnel face has a vehicle C to go out, the E intersection directly acts as a red light: e car waits for, after the C car passes E crossing, E car side can normally pass to D face, as shown in FIG. 13.

(3) If the D tunnel face has a vehicle D to go out, the E intersection directly acts as a red light: e car waits for, after the D car passes E crossing, E car side can normally pass to D face, as shown in FIG. 14.

The passing rule of the vehicle A on the face A for exiting comprises the following steps:

when the tunnel face A exits the vehicle A, under the default condition, the passing rule that the vehicle E at the E hole enters the tunnel face A is taken as the reference, and if special conditions are met:

① The E-car has arrived between the E-intersection and the B-intersection as shown in FIG. 15;

② The E vehicle has arrived between the B intersection and the A intersection as shown in FIG. 16;

and waiting for the vehicle A, and passing by a default passing rule that the vehicle E at the E hole enters the face A after the vehicle E reaches the face A.

The passing rule of the vehicle B coming out of the face B comprises the following steps:

when the B tunnel face is driven out of the vehicle B, under the default condition, taking the passing rule that the vehicle E at the E tunnel opening enters the B tunnel face as the standard, if special conditions are met:

If the E vehicle arrives between the E intersection and the B intersection, the B vehicle waits in situ, and after the E vehicle arrives at the B tunnel face, the E vehicle enters the B tunnel face according to the default passing rule of the E vehicle at the E tunnel mouth, as shown in fig. 17;

And if the A vehicle reaches the position between the intersection B and the intersection A, the B vehicle waits in situ, and after the A vehicle passes through the intersection E, the A vehicle enters the tunnel face B according to the default passing rule of the E vehicle at the intersection E, as shown in fig. 18.

The passing rule of the C vehicles on the face of the C comprises the following steps:

when the C tunnel face drives out of the vehicle C, under the default condition, taking the passing rule that the vehicle E vehicle at the E tunnel opening enters the C tunnel face as the reference, if special conditions are met:

The E-car has arrived between the E-intersection and the D-intersection as shown in FIG. 19;

The E-car has arrived between the D-intersection and the C-intersection as shown in FIG. 20;

And C, waiting for the E vehicle to pass through the C tunnel face according to the passing rule that the E vehicle of the default E tunnel entrance enters the C tunnel face after the E vehicle reaches the C tunnel face.

The passing rule of the D vehicles on the face of the D hand face comprises the following steps:

When the D tunnel face drives out of the vehicle D, the default condition is that the passing rule that the vehicle E vehicle at the E tunnel opening enters the D tunnel face is based, if special conditions are met:

E vehicles reach the position between the E intersection and the D intersection, D vehicles wait in situ, and after the E vehicles reach the D tunnel face, the E vehicles enter the D tunnel face according to the default passing rule of the E vehicles at the E tunnel opening, as shown in fig. 21;

And C vehicles reach a position between the C intersection and the D intersection, D vehicles wait in situ, and after the C vehicles pass through the E intersection, the C vehicles enter the D tunnel face according to the default passing rule of the vehicles E at the E entrance, as shown in fig. 22.

According to the traffic light configuration rule, the traffic light configuration rule is set, the rule templates are set through the background (for example, the behavior rule that vehicles come in and out is taken out first, the base station senses that vehicles exist in the direction of the exit, a traffic light system is utilized to remind opposite vehicles, vehicles are forbidden temporarily, vehicles in the direction of the entrance can normally pass after the vehicles go out, different rule templates are applied to each work area, for example, the work area uses a preferential vehicle-out template, traffic lights are matched in rule, and therefore vehicles are led to pass preferentially, and vehicles enter and wait for passing.

The method for controlling the traffic and traffic light of the tunnel construction vehicles, which is designed by the embodiment, carries out guiding control on engineering vehicles entering the tunnel, reduces vehicle idle running, saves resources and time cost, is simple and convenient to control and has perfect functions; by the application of the platform intelligent technology, the vehicle velocimeter, the vehicle base station and the traffic light are linked, so that manual intervention is reduced; after the tunnel is closed or before blasting in the tunnel, traffic light colors can be uniformly set in the background, vehicles and personnel are prohibited from entering the tunnel, and the traffic efficiency of vehicle dispatching and emergency handling efficiency of accidents are effectively improved.

The embodiment of the application also provides a computer device, a schematic structural diagram of which is shown in fig. 23, the computer device comprises: input means 23, output means 24, memory 22 and processor 21; the memory 22 is configured to store one or more programs; when the one or more programs are executed by the one or more processors 21, the one or more processors 21 implement the tunnel construction vehicle passing and traffic red-green light control method as provided in the above-described embodiments; wherein the input device 23, the output device 24, the memory 22 and the processor 21 may be connected by a bus or otherwise, for example in fig. 23 by a bus connection.

The memory 22 is used as a readable storage medium of a computing device and can be used for storing software programs and computer executable programs, and the program instructions corresponding to the tunnel construction vehicle passing and traffic red-green light control method are provided in the embodiment of the application; the memory 22 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for functions; the storage data area may store data created according to the use of the device, etc.; in addition, memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device; in some examples, memory 22 may further comprise memory located remotely from processor 21, which may be connected to the device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The input means 23 is operable to receive input numeric or character information and to generate key signal inputs relating to user settings and function control of the device; the output device 24 may include a display device such as a display screen.

The processor 21 executes various functional applications of the apparatus and data processing by running software programs, instructions and modules stored in the memory 22, i.e., implementing the tunnel construction vehicle traffic and traffic light control method described above.

The computer equipment provided by the embodiment can be used for executing the tunnel construction vehicle passing and traffic red and green lamp control method provided by the embodiment, and has corresponding functions and beneficial effects.

Embodiments of the present application also provide a storage medium containing computer executable instructions, which when executed by a computer processor, are for performing the tunnel construction vehicle traffic and traffic light control method as provided by the above embodiments, the storage medium being any of various types of memory devices or storage devices, the storage medium comprising: mounting media such as CD-ROM, floppy disk or tape devices; computer system memory or random access memory, such as DRAM, DDRRAM, SRAM, EDORAM, rambus (Rambus) RAM, or the like; nonvolatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc.; the storage medium also includes other types of memory or combinations thereof; in addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a second, different computer system, the second computer system being connected to the first computer system through a network (such as the internet); the second computer system may provide program instructions to the first computer for execution. Storage media includes two or more storage media that may reside in different locations (e.g., in different computer systems connected by a network). The storage medium may store program instructions (e.g., embodied as a computer program) executable by one or more processors.

The storage medium containing the computer executable instructions provided by the embodiment of the application is not limited to the tunnel construction vehicle traffic and traffic red-green light control method described in the above embodiment, and can also execute the related operations in the tunnel construction vehicle traffic and traffic red-green light control method provided by any embodiment of the application.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. The above description is only of the preferred embodiments of the present application and is not intended to limit the application, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present application should be included in the scope of the present application.

Claims

1. The method for controlling the traffic light of the tunnel construction vehicles is characterized by comprising the following steps:

2. The method for controlling traffic light and traffic light according to claim 1, wherein the method for obtaining the local feature area of the tunnel vehicle according to the gray level image of the tunnel vehicle and determining the neighboring feature matrix of each pixel based on the local feature area of the tunnel vehicle is as follows:

3. The method for controlling traffic light and traffic light according to claim 1, wherein the method for obtaining the vehicle monitoring local linear feature pair of each pixel point according to the neighboring feature matrix of each pixel point in the tunnel vehicle monitoring local feature area is as follows:

4. The method for controlling traffic light and traffic light according to claim 1, wherein the method for calculating the tunnel light interference difference coefficient of each pixel according to the vehicle monitoring local linear characteristic pair of each pixel in the tunnel vehicle monitoring local characteristic area is as follows:

5. The method for controlling traffic light and traffic light according to claim 1, wherein the method for obtaining the tunnel vehicle monitoring interference characteristic value according to the tunnel light interference difference coefficient of each pixel point in the tunnel vehicle monitoring local characteristic region is as follows:

6. The method for controlling traffic light and traffic light according to claim 5, wherein the method for classifying the tunnel light interference difference coefficients of all pixels in each local feature area of each tunnel vehicle and calculating the tunnel detection interference distribution feature value corresponding to each group of data comprises the following steps:

7. The method for controlling traffic and traffic lights and lights according to claim 1, wherein the specific method for obtaining the tunnel interference filter window coefficient of the tunnel vehicle monitoring gray level image according to the tunnel vehicle monitoring interference characteristic value comprises the following steps:

8. The method for controlling traffic light and traffic light according to claim 1, wherein the method for obtaining the noise-reduced monitoring gray image of the tunnel vehicle based on the noise-reduced monitoring gray image of the tunnel vehicle and the vehicle label information comprises the following steps:

9. The method for controlling traffic and traffic lights in a tunnel according to claim 8, wherein the method for controlling traffic lights to realize real-time control of traffic operations in tunnel construction according to the determination result of congestion condition is as follows:

determining the number of vehicles in each tunnel positive hole and inclined shaft according to the number of segments in the traffic chain corresponding to the tunnel positive hole and the inclined shaft; presetting a deviation threshold value between the number of vehicles in a tunnel positive tunnel and an inclined shaft and a reference traffic chain according to construction requirements; calculating the difference value between the real-time vehicle quantity in the tunnel positive tunnel and the inclined shaft and the vehicle quantity threshold value corresponding to the tunnel construction section; when the difference value is smaller than a preset deviation threshold value, the phenomenon that the tunnel positive hole and the inclined shaft are not jammed is considered; and when the difference value is larger than a preset deviation threshold value, the congestion risk in the tunnel positive hole and the inclined shaft is considered to be increased, traffic light linkage is controlled, and the congestion risk is reported.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that the processor, when executing the computer program, carries out the steps of the tunnel construction vehicle traffic and traffic light control method according to any one of claims 1-9.