Road surface asphalt temperature monitoring method, device and equipment and storage medium

Abstract

The invention provides a road surface asphalt temperature monitoring method, device and equipment and a storage medium, and belongs to the field of temperature monitoring, and the method comprises the steps: extracting the three-primary color brightness value of a labeled sample temperature field image and the shooting angle of the sample temperature field image; constructing a correlation expression between the brightness value and the shooting angle and the temperature based on the principle of a two-color temperature measurement method; substituting the annotation data into the correlativity, and determining original parameters of the correlativity; acquiring a temperature field image of asphalt on the surface of the target road section and an actual shooting angle corresponding to the temperature field image; and extracting a target three-primary color brightness value of the temperature field image, and substituting the target three-primary color brightness value and the actual shooting angle into the adjusted correlation expression to obtain the temperature of the asphalt on the surface of the target road section. Therefore, the influence on the temperature monitoring result caused by different monitoring angles of a fixed monitoring point position can be eliminated, and the accuracy of the temperature monitoring result and the monitoring effect of pavement asphalt temperature monitoring are improved.

Description

Technical Field

[0001] The present invention belongs to the field of temperature monitoring, and particularly relates to a method, device, equipment and storage medium for monitoring the temperature of asphalt on the road surface. Background Art

[0002] In pavement asphalt construction, temperature monitoring is crucial. The performance of asphalt is closely related to temperature. Appropriate temperature can ensure the construction quality, guarantee the strength, durability and flatness of the road surface, and improve the overall quality of highway construction.

[0003] Currently, during pavement asphalt construction, there are multiple different stages for temperature monitoring of asphalt, such as temperature monitoring during asphalt transportation, temperature monitoring when asphalt is discharged, temperature monitoring during asphalt paving, temperature monitoring during asphalt compaction, etc. Usually, non-contact temperature monitoring methods are adopted.

[0004] In the temperature monitoring after asphalt paving, it is mainly to observe whether the temperature of asphalt reaches the compaction standard before compaction. For example, when compacting asphalt at too high a temperature, it may cause asphalt aging and bleeding, increasing the construction difficulty. While when the temperature is too low, it will result in insufficient compaction degree of asphalt and too large void ratio, affecting the overall construction quality of asphalt. Therefore, by monitoring the temperature of paving asphalt, the compaction quality of asphalt can be ensured as much as possible.

[0005] In the prior art, general temperature monitoring is usually carried out by manual collection or by a device fixed at a certain point. Through methods such as radiation temperature measurement, continuous temperature monitoring is carried out on a certain area of asphalt. For the repair of a damaged point on the road surface, due to the small range and short time of the object to be monitored, manual collection is generally adopted. However, for newly paved roads, for a long asphalt paving section to be compacted, the monitoring range is large. Therefore, in the prior art, temperature collection is usually carried out by a device at a fixed point.

[0006] However, in continuous temperature monitoring, the monitoring instrument needs to collect the temperature of a large area of road section. For a monitoring instrument at a fixed point, when collecting the temperature of a large range of road section, due to the wide collection surface, for the monitoring instrument, factors such as distance, light and other possible factors are likely to cause deviations in the temperature monitoring of asphalt at different points on the road surface. Therefore, in this case, only a high-precision temperature collection can be ensured for a small area of the road surface within a fixed range, while for other road surfaces far from this fixed range, it will lead to the problems of low accuracy of temperature collection and poor monitoring effect. Summary of the Invention

[0007] In order to solve the problem of low accuracy of the detection instrument at a fixed point in monitoring the temperature of asphalt on a large range of road sections, the present invention provides a method, device, equipment and storage medium for monitoring the temperature of asphalt on the road surface.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] First, a method for monitoring the temperature of asphalt on the surface of a road is provided. The method includes:

[0010] Extracting the three-primary-color brightness values of the labeled sample temperature field image and the shooting angle of the sample temperature field image;

[0011] Constructing a correlation formula between the three-primary-color brightness values, the shooting angle, and the temperature based on the two-color temperature measurement method;

[0012] Substituting the labeled data of the sample temperature field image into the correlation formula to determine the original parameters of the correlation formula; the labeled data includes the three-primary-color brightness values, the shooting angle, and the corresponding labeled temperature;

[0013] Obtaining the temperature field image of the asphalt on the surface of the target section and the actual shooting angle corresponding to the temperature field image;

[0014] Extracting the target three-primary-color brightness values of the temperature field image of the asphalt on the surface of the target section, and substituting the target three-primary-color brightness values and the actual shooting angle into the correlation formula to obtain the temperature of the asphalt on the surface of the target section.

[0015] Optionally, the constructing a correlation formula between the three-primary-color brightness values, the shooting angle, and the temperature based on the two-color temperature measurement method includes:

[0016] Taking multi-angle shots of the same target to determine the three-primary-color brightness values at different shooting angles;

[0017] Fitting multiple shooting angles and their corresponding three-primary-color brightness values to determine the angle influence coefficients corresponding to different primary-color brightness values;

[0018] Constructing a two-color temperature measurement formula based on the brightness value and the angle influence coefficient, and adding a correction coefficient to obtain the correlation formula.

[0019] Optionally, the formula for determining the angle influence coefficient is:

[0020] α i = β0 + β1 * A;

[0021] where α i is the angle influence coefficient of the i-th primary color, β0 and β1 are the relevant parameters obtained by fitting, and A is the shooting angle.

[0022] Optionally, the correlation formula is:

[0023]

[0024] Wherein, L1 and L2 are the brightness values of any two primary colors among the tristimulus brightness values, K1 and K2 are correction factors respectively, λ1 and λ2 are the wavelengths corresponding to L1 and L2 respectively, T is the temperature, C2 represents the second radiation constant, ε(λ,T) is a dimensionless parameter representing the spectral emissivity, and α1 and α2 are the angular influence factors corresponding to L1 and L2 determined by the shooting angle respectively.

[0025] Optionally, before substituting the target tristimulus brightness values and the actual shooting angle into the relevant relational expression, the parameters of the relevant relational expression are also adjusted, including:

[0026] Substitute the original parameters into the relevant relational expression for verification to obtain the measured temperatures corresponding to multiple pairs of primary colors of the sample temperature field image;

[0027] Take the difference between the different measured temperatures corresponding to multiple pairs of primary colors as the loss value, and adjust the original parameters of the relevant relational expression with the goal of minimizing the loss value.

[0028] Optionally, taking the difference between the different measured temperatures corresponding to multiple pairs of primary colors as the loss value and adjusting the original parameters of the relevant relational expression with the goal of minimizing the loss value includes:

[0029] Taking the marked temperature as the benchmark and minimizing the loss value as the goal, adjust the correction factor;

[0030] When the loss value is the smallest and the difference between the measured temperature and the marked temperature is within the preset error range, obtain the correction factor.

[0031] Optionally, substituting the target tristimulus brightness values and the actual shooting angle into the relevant relational expression to obtain the temperature of the asphalt on the surface of the target road section includes:

[0032] Extract the brightness values of any two primary colors among the target tristimulus colors of the temperature field image as a group, and substitute multiple groups of target brightness values and the corresponding actual shooting angles into the calculation respectively to obtain multiple groups of monitored temperatures;

[0033] Take the average value of multiple groups of monitored temperatures as the temperature of the asphalt on the surface of the target road section.

[0034] Secondly, a device for monitoring the temperature of asphalt on the surface of a road is also provided. The device includes:

[0035] An extraction module for extracting the tristimulus brightness values of the sample temperature field image with markings and the shooting angle of the sample temperature field image;

[0036] A construction module for constructing a relevant relational expression between the tristimulus brightness values, the shooting angle and the temperature based on the two-color temperature measurement method;

[0037] A determination module, configured to substitute the annotation data of the sample temperature field image into a relevant relational expression to determine the original parameters of the relevant relational expression; the annotation data includes the tricolor brightness values, the shooting angle, and the corresponding annotated temperature.

[0038] An acquisition module, configured to acquire the temperature field image of the asphalt on the surface of the target road section and the shooting angle corresponding to the temperature field image.

[0039] A measurement module, configured to extract the target tricolor brightness values of the temperature field image of the asphalt on the surface of the target road section, and substitute the target tricolor brightness values and the actual shooting angle into the relevant relational expression to obtain the temperature of the asphalt on the surface of the target road section.

[0040] In addition, a computer-readable storage medium is also provided. The storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for monitoring the temperature of asphalt on the surface of a road is implemented.

[0041] Finally, a computer device is also provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above-mentioned method for monitoring the temperature of asphalt on the surface of a road is implemented.

[0042] The method for monitoring the temperature of asphalt on the surface of a road provided by the present invention has the following beneficial effects:

[0043] Firstly, relevant data of the sample temperature field image is acquired. Secondly, a relevant relational expression is constructed using the principle of the two-color temperature measurement method, which can correlate the brightness value information and the shooting angle in the image with the temperature. Based on the actual situation of large-scale monitoring by the monitoring instrument, improvements are made on the basis of the two-color temperature measurement method. When the monitoring instrument at a fixed point performs large-scale temperature acquisition, the influence of the angle problem on the accuracy of temperature monitoring can be eliminated, so that the monitoring instrument can avoid the influence of the angle factor in large-scale temperature acquisition, which is beneficial to improving the accuracy of temperature monitoring for the asphalt paving on the road surface; and improving the monitoring effect of the monitoring instrument on the temperature of the road surface asphalt. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention and their design schemes, the accompanying drawings required for the present embodiments will be briefly introduced below. The accompanying drawings in the following description are only partial embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 It is a schematic flowchart of a method for monitoring the temperature of asphalt on the surface of a road provided by the present invention according to an exemplary embodiment.

[0046] Figure 2Schematic diagram of the correlation between brightness value and shooting angle provided by the present invention according to an exemplary embodiment.

[0047] Figure 3 Schematic diagram of a multi-channel network model provided by the present invention according to an exemplary embodiment.

[0048] Figure 4 Block diagram of a highway surface asphalt temperature monitoring device provided by the present invention according to an exemplary embodiment. Detailed implementation manners

[0049] In order to enable those skilled in the art to better understand the technical solution of the present invention and be able to implement it, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and cannot be used to limit the protection scope of the present invention.

[0050] The following will describe in detail the technical solutions provided by each embodiment of the present invention with reference to the accompanying drawings.

[0051] The present invention conducts control experiments on various conditions such as angle, lighting conditions, and distance respectively, and finds that when other conditions are the same, different angles have the greatest impact on the tricolor brightness values obtained by the monitoring instrument, resulting in the largest deviation in the temperature values finally obtained by the two-color temperature measurement method. Therefore, in order to solve the problem of low accuracy of the detection instrument at a fixed point in the prior art for monitoring the asphalt temperature of a large-range road surface, the present invention adds an angle influence coefficient on the basis of the two-color temperature measurement method to eliminate the temperature measurement deviation caused by the angle problem.

[0052] First of all, the present invention provides a method for monitoring the asphalt temperature of a highway surface, specifically as Figure 1 shown, including the following steps:

[0053] S101. Construct a correlation formula between the brightness value, shooting angle and temperature.

[0054] In this step, first extract the tricolor brightness values of the sample temperature field image with annotations and the shooting angle of the sample temperature field image; construct a correlation formula between the tricolor brightness values, shooting angle and temperature based on the two-color temperature measurement method.

[0055] Among them, the annotated sample data includes temperature field images taken at multiple different angles of the same target point obtained in advance.

[0056] In this step, a series of temperature field images with temperature annotations need to be collected as training samples. These images should cover different temperature ranges, lighting conditions, and shooting angles to ensure the generalization ability of the model. For each image, use image processing software (such as OpenCV, MATLAB, etc.) to extract the brightness values of its three primary colors (red, green, blue), and record the angle information (such as horizontal angle, vertical angle) during shooting. Then, construct a dataset based on the recorded data, including 100 asphalt pavement temperature field images taken at different time points and different weather conditions. Each image is attached with a corresponding temperature annotation (obtained by measuring with a contact thermometer). Use the OpenCV library to read these images and extract the brightness values of the red, green, and blue primary colors of each image. At the same time, record the shooting angle of each image. For example, the horizontal angle of a certain image is 30 degrees and the vertical angle is 45 degrees.

[0057] In one embodiment, for the construction of the relevant relational formula, it is necessary to first clarify the influence coefficient of the shooting angle on the brightness value; specifically, the same target can be shot from multiple angles to determine the brightness values of the three primary colors at different shooting angles; fit multiple shooting angles and their corresponding brightness values of the three primary colors to determine the angle influence coefficients corresponding to different primary color brightness values; construct a two-color temperature measurement formula based on the brightness value and the angle influence coefficient, and add a correction coefficient to obtain the relevant relational formula.

[0058] For example, shoot the same target from multiple different angles to ensure that each angle can fully display the three primary color characteristics of the target. For each shooting angle, record the brightness values of the three primary colors (red, green, blue). For the obtained data, first check the data integrity and remove outliers or invalid data. If the brightness values vary greatly at different angles or different temperatures, consider normalizing the data for better subsequent analysis, such as Figure 2 shown, record the change of the brightness values of RGB (three primary colors) with the angle. Then, perform a separate fit for each primary color. For each primary color (red, green, blue), use statistical methods (such as linear regression, polynomial regression, etc.) to fit the relationship between the shooting angle and the brightness value of this primary color. The purpose of this step is to obtain the trend of the brightness value of each primary color changing with the angle and obtain the influence factor of the angle on the brightness value. Based on this influence factor, a mathematical model related to the shooting angle (A) can be constructed. The present invention uses linear regression to fit the relationship between the angle and the brightness value, and further extracts the change law of the angle influence coefficient. The formula for determining this angle influence coefficient is:

[0059] α i = β0 + β1 * A;

[0060] where, α iis the angular influence coefficient of the i-th primary color, β0 and β1 are the relevant parameters obtained by fitting, and A is the shooting angle.

[0061] Based on the principle of two-color temperature measurement, by adding the above angular influence coefficient and correction coefficient, the relevant relational expression can be obtained as:

[0062]

[0063] Among them, L1 and L2 are the brightness values of any two primary colors in the tricolor brightness values respectively, K1 and K2 are the correction coefficients respectively, λ1 and λ2 are the wavelengths corresponding to L1 and L2 respectively, T is the temperature, C2 represents the second radiation constant, ε(λ,T) is a dimensionless parameter representing the spectral emissivity, and α1 and α2 are the angular influence coefficients corresponding to L1 and L2 determined by the shooting angle respectively. In this way, on the basis of two-color temperature measurement, considering the influence of the shooting angle on the brightness value, the angular influence coefficient is added, and the influence of the shooting angle on the tricolor brightness value is avoided as much as possible, which is beneficial to improving the accuracy of temperature monitoring.

[0064] S102. Substitute the annotation data of the sample temperature field image into the relevant relational expression to determine the original parameters of the relevant relational expression.

[0065] Among them, the annotation data includes the tricolor brightness values, the shooting angle, and the corresponding annotated temperature.

[0066] In one embodiment, for the constructed relevant relational expression, specific data also needs to be substituted to determine its parameters. Specifically, the annotation data of the sample temperature field image can be substituted, and by substituting multiple groups of annotation data and performing simultaneous elimination of terms, the parameters of the relevant relational expression can be calculated. It should be noted here that in order to avoid the influence of environmental factors on the wavelength, multiple groups of annotation data to be substituted need to be screened, and multiple groups of annotation data with the same primary color combination are selected, so as to reduce variables as much as possible and avoid the influence of unknown factors.

[0067] In addition, theoretically, the parameters calculated by multiple combinations of different primary color combinations should be the same, but due to the influence of various external factors, the parameters calculated by multiple combinations of different primary color combinations have errors in the end, and thus there is a difference between the predicted temperature and the annotated temperature. Therefore, a correction coefficient needs to be introduced to correct this error to improve the accuracy of the calculated parameters; the correction coefficient in the relevant relational expression will be processed later.

[0068] In another embodiment, the brightness values of different primary colors need to be substituted into the relevant relational expression to adjust the original parameters of the relevant relational expression to reduce errors.

[0069] In this step, it is necessary to substitute the original parameters into the relevant relational expressions, and then verify the relational expressions with the original parameters substituted. After substituting the original parameters into the relevant relational expressions, multiple sets of different dual primary colors with the same marked temperature in the sample temperature field and their corresponding shooting angles can be input into the relevant relational expressions to obtain the measured temperatures corresponding to multiple sets of dual primary colors at the same temperature in the sample temperature field image; the differences between the different measured temperatures corresponding to multiple sets of dual primary colors are used as loss values, and the original parameters of the relevant relational expressions are adjusted with the goal of minimizing the loss values.

[0070] In addition, for the correction coefficient, in order to improve the accuracy of the finally predicted temperature, with this marked temperature as the benchmark and the goal of minimizing the loss value, this correction coefficient is adjusted; when the loss value is the smallest and the difference between the measured temperature and the marked temperature is within the preset error range, this correction coefficient is obtained.

[0071] For example, in order to verify the accuracy of the relevant relational expressions, the relational expressions with the original parameters substituted can be applied to new samples, and the differences between the temperatures predicted by the model and the actually marked temperatures can be compared. In addition, in order to explore the possibility of the two-color temperature measurement method, any two sets of colors among red-green, red-blue, or green-blue can be tried to predict the temperature in this step, and the prediction effects under different combinations can be compared.

[0072] Specifically, a trained linear regression model can be used to predict the images in the validation set in the sample temperature field image, and the mean square error (MSE) between the predicted temperature and the actual temperature can be calculated. Then, the model is retrained respectively using three sets of colors, red-green, red-blue, and green-blue, as features, and their prediction performances are compared.

[0073] In one embodiment, after substituting the specific data of multiple combinations of different dual primary colors, multiple relational expressions of brightness values, shooting angles, and temperatures are obtained. Each relational expression can be mapped to a unit structure of a neural network, and then multiple relational expressions are constructed into a multi-channel neural network. The difference between the outputs of multiple channels is used as the loss function, and then the neural network is trained by machine learning methods to adjust the parameters of the neural network, completing the parameter adjustment of the relevant relational expressions.

[0074] When the brightness values of the three channels of RGB (three primary colors) of a pixel point are known, a relational expression can be constructed for one pixel point. Given the number of unknowns (i.e., the original parameters) in this relational expression, theoretically, relational expressions constructed from pixel points at corresponding numbers of different temperatures are required to solve this unknown. One unit structure can input one pixel point at the same time, so a multi-channel network model can be constructed using parallel unit structures corresponding to the number of this unknown at the same time. Specifically, as Figure 3 shown, a six-channel network model is constructed.

[0075] Taking the red-blue two-color temperature measurement method and the red-green two-color temperature measurement method as examples, since the temperature values theoretically measured by the red-blue two-color temperature measurement method and the red-green two-color temperature measurement method are equal, the present invention constructs a sub-model for each of the red-blue two-color temperature measurement method and the red-green two-color temperature measurement method according to the mechanism formula. After the two sub-models theoretically input the brightness values, they will output temperature values, and theoretically the temperature values are equal. However, there are unknown quantities to be trained when constructing the two sub-models. Before being trained well, the temperature values output by the two sub-models are not equal. Then, a loss value will be generated from these two different temperature values. Using this loss value as a constraint, a multi-channel neural network model that maps the brightness value to the predicted temperature is constructed.

[0076] However, using the difference between the temperature values predicted by the two sub-models as the loss value to train the multi-channel neural network model will result in the predicted parameters being proportional parameters of the true parameters. Therefore, it is necessary to adjust the parameters obtained by training based on the original parameters, and finally obtain the adjusted relevant relationship.

[0077] For example, multiple original parameters can be determined through multiple groups of different primary color brightness values, and the parameters obtained by training are adjusted. When the sum of the variances between the parameters obtained by training and the multiple original parameters is the smallest, the parameters obtained by training are determined as the adjusted parameters.

[0078] S103. Determine the asphalt temperature on the road surface of the target section through this relevant relationship.

[0079] Obtain the temperature field image of the asphalt on the surface of the target section and the actual shooting angle corresponding to the temperature field image. Extract the target three-primary-color brightness values of the temperature field image of the asphalt on the surface of the target section, and substitute the target three-primary-color brightness values and the actual shooting angle into the relevant relationship to obtain the temperature of the asphalt on the surface of the target section.

[0080] In this step, devices such as drones or cameras at fixed points can be used to capture the temperature field image of the target section and record the angle information during shooting. These images will be used for subsequent temperature prediction. For example, a drone can be used to conduct aerial photography of a certain section of the highway, or an image acquisition device fixed at a certain point to obtain the temperature field image of the asphalt on the surface of the highway. At the same time, information such as the flight altitude and heading of the drone is recorded for subsequent calculation of the shooting angle.

[0081] Then, image processing software is used to extract the three-primary-color brightness values of the target image, and these values and the shooting angle are substituted into the adjusted relevant relationship, so as to obtain the temperature distribution of the target section. For example, the OpenCV library can be used to extract the red, green, and blue brightness values of the temperature field image captured by the drone, and these values and the calculated shooting angle are substituted into the adjusted relevant relationship to obtain the temperature value of each pixel point, thereby obtaining the temperature distribution map of the entire section.

[0082] For example, take the brightness values of any two primary colors in the target three primary colors of the temperature field image as a group. Substitute multiple groups of brightness values and the corresponding actual shooting angles into the calculation respectively to obtain multiple groups of monitored temperatures. Take the average value of the multiple groups of monitored temperatures as the temperature of the asphalt on the surface of the target road section.

[0083] For the temperature distribution map of the entire road section, it can also be drawn into a road section - temperature or a two - dimensional chart of temperature - time for the same road section, so that relevant personnel can more intuitively understand the temperature of the asphalt on the surface of the target road section.

[0084] Using the above - mentioned method, first obtain the relevant data of the sample temperature field image, and secondly construct relevant relational expressions based on the principle of the two - color temperature measurement method, which can associate the brightness value information and shooting angle in the image with temperature. In this way, based on the actual situation of large - range monitoring by the monitoring instrument, an improvement is made on the basis of the two - color temperature measurement method. When the monitoring instrument at a fixed point performs large - range temperature acquisition, by eliminating the influence of the angle problem on the accuracy of temperature monitoring, the monitoring instrument can avoid the influence of angle factors in large - range temperature acquisition, which is beneficial to improving the accuracy of temperature monitoring for highway surface asphalt paving; improving the monitoring effect of the monitoring instrument on the temperature of road surface asphalt.

[0085] Secondly, the present invention also provides a device for monitoring the temperature of asphalt on the surface of a road, as Figure 4 shown, including:

[0086] An extraction module 401, configured to extract the brightness values of the three primary colors of the annotated sample temperature field image and the shooting angle of the sample temperature field image.

[0087] A construction module 402, configured to construct a relevant relational expression between the brightness values of the three primary colors, the shooting angle, and the temperature based on the two - color temperature measurement method.

[0088] A determination module 403, configured to substitute the annotation data of the sample temperature field image into the relevant relational expression to determine the original parameters of the relevant relational expression; the annotation data includes the brightness values of the three primary colors, the shooting angle, and the corresponding annotated temperature.

[0089] An acquisition module 404, configured to acquire the temperature field image of the asphalt on the surface of the target road section and the shooting angle corresponding to the temperature field image.

[0090] A measurement module 405, configured to extract the brightness values of the target three primary colors of the temperature field image of the asphalt on the surface of the target road section, and substitute the brightness values of the target three primary colors and the actual shooting angle into the relevant relational expression to obtain the temperature of the asphalt on the surface of the target road section.

[0091] Using the above device, first obtain the relevant data of the sample temperature field image, and secondly construct relevant relational expressions based on the principle of the two-color thermometry method, which can associate the brightness value information and the shooting angle in the image with the temperature. In this way, based on the actual situation of large-scale monitoring by the monitoring instrument, an improvement is made on the basis of the two-color thermometry method. When the monitoring instrument at a fixed point performs large-scale temperature acquisition, by eliminating the influence of the angle problem on the accuracy of temperature monitoring, the monitoring instrument can avoid the influence of the angle factor in large-scale temperature acquisition, which is beneficial to improving the accuracy of temperature monitoring for highway surface asphalt paving; improving the monitoring effect of the monitoring instrument on the temperature of the road surface asphalt.

[0092] The present invention also provides a computer-readable storage medium, which stores a computer program, and the computer program can be used to execute the above Figure 1 steps of the highway surface asphalt temperature monitoring method provided.

[0093] The present invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the above Figure 1 steps of the highway surface asphalt temperature monitoring method provided.

[0094] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can also be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0096] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in the process Figure 1 a process or processes and / or blocks Figure 1 a block or blocks.

[0097] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the process Figure 1 a process or processes and / or blocks Figure 1 a block or blocks.

[0098] It should be noted that the above specific implementation can enable those skilled in the art to understand the present invention more comprehensively, but does not limit the present invention in any way. Therefore, although this specification has described the present invention in detail, those skilled in the art should understand that the present invention can still be modified or equivalently replaced; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered by the protection scope of the patent of the present invention. Any reference signs in the claims should not be construed as limiting the claimed claim.

Claims

1. A method for monitoring the temperature of asphalt on a highway surface, characterized in that: The method comprises: Extract the three primary color brightness values ​​of the labeled sample temperature field image and the shooting angle of the sample temperature field image; Based on the two-color temperature measurement method, the correlation equation between the brightness values ​​of the three primary colors, the shooting angle and the temperature is constructed; Substituting the annotated data of the sample temperature field image into the correlation equation to determine the original parameters of the correlation equation; the annotated data includes the brightness values ​​of the three primary colors, the shooting angle and the corresponding annotated temperature; Acquire the temperature field image of the asphalt on the target road section surface and the actual shooting angle corresponding to the temperature field image; The target three-primary-color brightness values ​​of the temperature field image of the target road section surface asphalt are extracted, and the target three-primary-color brightness values ​​and the actual shooting angle are substituted into the correlation equation to obtain the temperature of the target road section surface asphalt.

2. A method for monitoring the temperature of asphalt on a highway surface according to claim 1, characterized in that: The correlation equation between the brightness values ​​of the three primary colors, the shooting angle and the temperature constructed based on the two-color temperature measurement method includes: Shoot the same target from multiple angles to determine the brightness values ​​of the three primary colors at different shooting angles; Fitting multiple shooting angles and their corresponding three-primary color brightness values ​​to determine the angle influence coefficients corresponding to different primary color brightness values; The two-color temperature measurement formula based on brightness value and angle influence coefficient was constructed, and the correction coefficient was added to obtain the correlation equation.

3. A method for monitoring the temperature of asphalt on a highway surface according to claim 2, characterized in that: The formula for determining the angle influence coefficient is: α i ＝β0+β1*A； Among them, α i is the angular influence coefficient of the i-th primary color, β0 and β1 are the relevant parameters obtained by fitting, and A is the shooting angle.

4. A method for monitoring the temperature of asphalt on a highway surface according to claim 1, characterized in that: The related relationship is: Among them, L1 and L2 are the brightness values ​​of any two primary colors in the three primary color brightness values, K1 and K2 are correction coefficients, λ1 and λ2 are the wavelengths corresponding to L1 and L2, T is temperature, C2 represents the second radiation constant, ε(λ,T) is a dimensionless parameter representing the spectral emissivity, α1 and α2 are the angle influence coefficients corresponding to L1 and L2 determined by the shooting angle.

5. A method for monitoring the temperature of asphalt on a highway surface according to claim 1, characterized in that: Before substituting the target three primary color brightness values ​​and the actual shooting angle into the correlation equation, the parameters of the correlation equation are adjusted, including: Substitute the original parameters into the correlation equation for verification, and obtain the measured temperatures corresponding to multiple groups of dual primary colors of the sample temperature field image; The difference between different measured temperatures corresponding to the multiple sets of dual primary colors is taken as a loss value, and the original parameters of the correlation equation are adjusted with the goal of minimizing the loss value.

6. A method for monitoring the temperature of asphalt on a highway surface according to claim 5, characterized in that: The method of taking the difference between different measured temperatures corresponding to the multiple sets of dual primary colors as the loss value and adjusting the original parameters of the correlation equation with the goal of minimizing the loss value includes: Taking the marked temperature as a reference and minimizing the loss value as a goal, adjusting the correction coefficient; When the loss value is minimum and the difference between the measured temperature and the marked temperature is within a preset error range, the correction coefficient is obtained.

7. A method for monitoring the temperature of asphalt on a highway surface according to claim 1, characterized in that: Substituting the target three primary color brightness values ​​and the actual shooting angle into the relevant equation, the temperature of the asphalt on the target road section surface is obtained: The brightness values ​​of any two primary colors in the target three primary colors of the temperature field image are extracted as a group, and multiple groups of brightness values ​​and corresponding actual shooting angles are respectively substituted into the calculation to obtain multiple groups of monitored temperatures; The average of multiple groups of monitored temperatures is used as the surface asphalt temperature of the target road section.

8. A road surface asphalt temperature monitoring device, characterized in that: The device comprises: An extraction module, used to extract the three primary color brightness values ​​of the labeled sample temperature field image and the shooting angle of the sample temperature field image; A construction module, used for constructing a correlation equation between the brightness values ​​of three primary colors, the shooting angle and the temperature based on a two-color temperature measurement method; A determination module, used to substitute the annotated data of the sample temperature field image into the correlation equation to determine the original parameters of the correlation equation; the annotated data includes the brightness values ​​of the three primary colors, the shooting angle and the corresponding annotated temperature; An acquisition module is used to acquire a temperature field image of the asphalt on the surface of the target road section and an actual shooting angle corresponding to the temperature field image; The measuring module is used to extract the target three-primary color brightness values ​​of the temperature field image of the target road section surface asphalt, substitute the target three-primary color brightness values ​​and the actual shooting angle into the correlation formula to obtain the temperature of the target road section surface asphalt.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program.

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