Phase-scan based pavement microtexture depth evaluation method and system

Abstract

The application discloses a phase scanning based road surface micro-texture depth evaluation method and system, and particularly relates to the field of road surface micro-texture depth evaluation.The scheme comprises the following steps: a laser of a preset wavelength is emitted to a road surface by a laser phase scanning device; an original phase difference signal of the road surface micro-texture is collected by a phase detection unit; a laser incidence angle, an illumination angle and road surface image data are synchronously obtained by an environment sensing unit; an initial micro-texture depth of the road surface micro-texture is calculated; texture feature extraction is performed on the obtained road surface image data; the initial micro-texture depth is dynamically corrected by combining an illumination correction factor obtained based on the illumination angle; a final micro-texture depth is obtained; and the final micro-texture depth is converged until the final micro-texture depth converges.The application realizes high-precision and high-adaptability evaluation of the road surface micro-texture depth by means of illumination correction, texture density correction, noise correction and dynamic iteration optimization, and by combining image data processing, three-dimensional image generation and other technologies.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of road surface micro-texture depth evaluation, in particular to a road surface micro-texture depth evaluation method and system based on phase scanning. BACKGROUND

[0002] Road surface micro-texture is a core factor affecting road surface skid resistance, and its depth is directly related to vehicle braking distance, tire wear and traffic safety. Traditional evaluation methods (such as sand paving method and laser profilometer) have problems such as low efficiency of contact measurement and environmental interference of optical method. With the development of image data processing and three-dimensional reconstruction technology, non-contact evaluation methods based on phase scanning have gradually become the mainstream.

[0003] However, in the existing evaluation method based on phase scanning, the laser is assumed to be vertically incident, and the change of illumination direction in actual detection is ignored. According to the Lambert cosine law, oblique illumination will cause the intensity of road surface reflected light to decay, which directly leads to the underestimation of the phase difference measurement value. The illumination angle interference is not quantitatively corrected, and the initial depth is underestimated. In addition, when the road surface micro-texture is dense, multi-peak reflection will cause the phase signals to cancel each other out. The existing method directly uses the original phase difference to calculate the depth without considering the influence of texture spatial distribution on the phase signal, which will cause the texture density superposition effect to be uncompensated, and the depth of dense texture area to be distorted. In addition, the phase scanning device has sensor noise, which is manifested as random fluctuations in the phase difference. In low-texture areas, the amplitude of the noise signal is close to that of the real texture signal. The existing method does not quantitatively correct the phase noise, which leads to the noise being misjudged as "pseudo-texture depth", and the problem of false depth caused by phase noise not being eliminated.

[0004] Therefore, the present application provides a road surface micro-texture depth evaluation method and system based on phase scanning to solve the problems raised in the background. SUMMARY

[0005] The technical problem solved by the present application is to provide a road surface micro-texture depth evaluation method and system based on phase scanning to realize adaptive correction of illumination angle, compensate for dense texture superposition effect, and improve the accuracy in low-texture areas.

[0006] To solve the above problems, the present application provides the following technical solutions:

[0007] In a first aspect, one embodiment of the present application provides a road surface micro-texture depth evaluation method based on phase scanning, which comprises the following steps:

[0008] The laser phase scanning device emits laser of preset wavelength to the road surface, the original phase difference signal of the road microtexture is collected by the phase detection unit, the laser incidence angle, the illumination angle and the road image data are synchronously obtained by the environment sensing unit, the original phase difference signal is phase unwrapping processed to eliminate the phase jump interference, and the continuous phase difference sequence is obtained;

[0009] According to the laser wavelength, the continuous phase difference sequence and the laser incidence angle, the initial microtexture depth of the road microtexture is calculated;

[0010] The texture feature extraction is performed on the obtained road image data to obtain the texture density parameter, and the initial microtexture depth is dynamically corrected by combining the illumination correction factor obtained based on the illumination angle to obtain the corrected microtexture depth;

[0011] The false depth component is removed by phase noise analysis to obtain the final microtexture depth;

[0012] The root mean square error of the final microtexture depth in two continuous iterations is less than a preset threshold as a convergence condition, and the initial microtexture depth, the corrected microtexture depth and the final microtexture depth are repeatedly calculated until the final microtexture depth converges, and the final microtexture depth is output.

[0013] Further, the determination method of the initial microtexture depth is as follows:

[0014] The laser phase scanning device emits laser of preset wavelength to the road surface, the original phase difference signal of the road microtexture is collected by the phase detection unit, the original phase difference signal is phase unwrapping processed to obtain the continuous phase difference sequence;

[0015] The laser incidence angle is measured by the inclination sensor as the included angle between the laser incidence direction and the road normal, and the illumination angle is measured by the illumination sensor as the included angle between the environmental illumination direction and the road normal;

[0016] The continuous phase difference sequence is converted into initial depth: according to the laser wavelength, the phase difference in the continuous phase difference sequence and the cosine value of the laser incidence angle, the theoretical depth under the current incidence condition is calculated, then the theoretical depth is compensated by taking the cosine value of the illumination angle as the illumination correction factor, and the initial microtexture depth of the road microtexture is obtained.

[0017] Further, the illumination correction factor is input according to a preset rule based on the illumination angle, and specifically as follows:

[0018] If the illumination angle is 0°, the illumination correction factor is set to 1;

[0019] If the illumination angle is 30°, the illumination correction factor is set to 0.866;

[0020] If the illumination angle is 45°, the illumination correction factor is set to 0.707;

[0021] If the illumination angle is 60°, the illumination correction factor is set to 0.5;

[0022] If the illumination angle is 90°, the illumination correction factor is set to 0.

[0023] Further: the determination method of the corrected micro-texture depth is:

[0024] Image segmentation and edge detection are performed on the road surface image data, the peak point area of the road surface micro-texture is extracted by a threshold segmentation algorithm, and the number of peak points in a unit area is counted as the micro-texture spatial density;

[0025] On the basis of the initial micro-texture depth, a correction coefficient containing the product of the micro-texture spatial density and the illumination correction factor is constructed, and the corrected micro-texture depth is calculated by multiplying the initial micro-texture depth and the correction coefficient;

[0026] Wherein, the correction coefficient increases with the increase of the micro-texture spatial density, so as to compensate for the phase signal cancellation problem caused by multi-peak reflection in dense texture area.

[0027] Further: the determination method of the final micro-texture depth is:

[0028] Select samples in the flat area of the road surface image data, and calculate the standard deviation of the phase difference sequence as the phase measurement noise;

[0029] Divide the product of the phase measurement noise and the micro-texture spatial density by the phase difference to obtain a first proportion of the noise false depth to the corrected depth, and remove the noise false depth corresponding to the product of the first proportion and the corrected micro-texture depth from the corrected micro-texture depth to obtain the final micro-texture depth.

[0030] Further: based on the conversion of the continuous phase difference sequence to the initial micro-texture depth, the relationship between the inverse phase difference and the depth can be solved, and when the initial micro-texture depth is not equal to the final micro-texture depth, the phase difference deviation value is obtained, and the corrected phase difference is obtained according to the sum of the phase difference deviation value and the phase difference to replace the phase difference in iteration.

[0031] Further: if the input of the illumination correction factor is 0, it is considered as invalid illumination, and the data needs to be re-collected.

[0032] In a second aspect, the embodiments of the present application also provide a phase scanning based road surface micro-texture depth evaluation system, comprising:

[0033] A phase data acquisition module: comprising a laser emission unit for providing a preset wavelength laser, a phase detection unit for acquiring an original phase difference signal, and an environment sensing unit for acquiring a laser incidence angle, an illumination angle, and road image data;

[0034] An initial depth calculation unit: connected with the phase data acquisition module, performing phase-depth conversion and illumination correction, and outputting an initial micro-texture depth;

[0035] A texture correction unit: connected with the initial depth calculation unit, performing texture density extraction and coupling correction, and outputting a corrected micro-texture depth;

[0036] A noise optimization unit: connected with the texture correction unit, performing phase noise calculation, false depth elimination, and dynamic iteration optimization, and outputting a final micro-texture depth;

[0037] A result output module: connected with the noise optimization unit, generating a road surface micro-texture depth visualization report and a data file.

[0038] The above scheme has the following effects:

[0039] The illumination correction factor obtained by the illumination angle measured by the illumination sensor in real time can dynamically compensate for the phase signal attenuation under different illuminations, the illumination correction factor is integrated into the initial depth calculation for dynamic adjustment, the phase signal attenuation caused by oblique illumination is reversely compensated, the micro-texture spatial density is extracted through image analysis, the “texture density-illumination intensity” cooperative correction coefficient is constructed, the depth correction amplitude is dynamically adjusted, the problem of mutual cancellation of phase signals caused by multi-peak reflection and underestimation of depth when the road surface texture is dense is solved, different texture distribution scenarios are dynamically adapted, the phase measurement noise is calculated by the phase standard deviation of the flat area, the “noise-texture density-phase difference” coupling correction term is constructed, the false noise component is eliminated from the corrected micro-texture depth, the problem of false depth signal caused by random noise of the phase scanning device in the low texture area is solved, and the authenticity of the depth evaluation result is further ensured, providing reliable data support for road surface anti-skid performance analysis.

[0040] In addition, the convergence condition is that the root mean square error of the final micro-texture depth of the two consecutive iterations is less than a preset threshold, the problem of insufficient convergence in complex texture areas or low efficiency in simple texture areas caused by fixed iteration number is solved, and the overall evaluation performance is improved through dynamic judgment of the convergence state. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1The method steps of the road surface micro-texture depth evaluation method based on phase scanning of the present application are shown in the following. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be clearly and completely introduced below with reference to the drawings in the embodiments of the present application.

[0043] Embodiment one, please refer to Figure 1 , the road surface micro-texture depth evaluation method based on phase scanning, the method comprises the following steps:

[0044] Step 1: Emit a laser of a preset wavelength to the road surface through a laser phase scanning device, collect the original phase difference signal of the road surface micro-texture by using a phase detection unit, synchronously acquire the laser incidence angle, the illumination angle and the road surface image data through an environmental sensing unit, and perform phase unwrapping processing on the original phase difference signal to eliminate phase jump interference and obtain a continuous phase difference sequence.

[0045] In this embodiment, the laser phase scanning device is used to emit a laser beam to the road surface, the reflected light signal is received by the phase detection unit after the laser beam is reflected by the road surface micro-texture, and the original phase difference signal is collected, at the same time, the following parameters are acquired through the environmental sensing unit:

[0046] Laser incidence angle: measured by an inclination sensor installed on the laser emitter, indicating the angle between the laser beam and the normal direction of the road surface;

[0047] Illumination angle: measured by an illumination sensor, indicating the angle between the environmental illumination direction and the normal of the road surface;

[0048] Road surface image data: synchronously collected by a high-resolution CCD camera, stored in RAW format for subsequent processing.

[0049] The collected original phase difference signal is subjected to phase unwrapping processing, the Goldstein branch cut algorithm is used to eliminate phase jump, and a continuous phase difference sequence is obtained, and the specific processing flow is as follows:

[0050] Calculate the phase gradient;

[0051] Identify the phase jump point (the jump amplitude is greater than π);

[0052] Smooth the phase curve by path integral method to obtain a continuous phase difference sequence.

[0053] It can be understood that the phase unwrapping processing is to eliminate the 2π jump caused by the periodicity of the phase to ensure the continuity and physical reasonableness of the phase difference sequence.

[0054] At this point, the collection and preprocessing of the phase data are completed, and a continuous phase difference sequence that can be used for depth calculation is obtained.

[0055] Step 2: Calculate the initial microtexture depth of the road surface microtexture based on the laser wavelength, continuous phase difference sequence and laser incident angle.

[0056] The specific method for determining the depth in this embodiment is as follows: the theoretical depth under the current incident conditions is calculated based on the laser wavelength, the phase difference in the continuous phase difference sequence, and the cosine value of the laser incident angle. Then, the theoretical depth is compensated by using the cosine value of the illumination angle as an illumination correction factor, and the initial micro-texture depth of the road surface is obtained.

[0057] It is important to understand that, since the phase difference is proportional to the optical path difference, and the optical path difference is: The depth of the microtexture is: (The round trip route is halved), therefore the core item This is the physical essence of phase-to-depth transformation, and based on the above determination method, the calculation formula for its initial microtexture depth is as follows:

[0058] ;

[0059] In the formula:

[0060] S ini λ represents the initial microtexture depth (uncorrected), and λ is the laser wavelength obtained by phase scanning excitation. The phase difference is caused by the micro-texture of the road surface, cosθ is the laser incident angle, i.e., the angle between the laser and the road surface normal, and G is the illumination correction factor. In this calculation formula... Mapping phase values ​​to depth values ​​falls under the category of "image-to-image conversion within the image plane." Specifically, it involves converting the pixel values ​​of a phase image from the "phase domain" to the "depth domain" to generate an initial depth map. Subsequently, an illumination correction factor G is introduced to correct phase deviations caused by uneven illumination through image enhancement techniques. This falls under the category of "illumination correction" within the field of "image enhancement or restoration."

[0061] The illumination correction factor is set based on the illumination angle and according to preset rules, as follows:

[0062] If the illumination angle is 0°, the illumination correction factor is set to 1;

[0063] If the illumination angle is 30°, then the illumination correction factor is set to 0.866;

[0064] If the illumination angle is 45°, then the illumination correction factor is set to 0.707;

[0065] If the illumination angle is 60°, then the illumination correction factor is set to 0.5;

[0066] If the illumination angle is 90°, then the illumination correction factor is set to 0;

[0067] And if the light correction factor input is 0, it is considered invalid light, and the data needs to be reacquired.

[0068] Further, based on the Lambert cosine law, the road surface micro-texture surface (such as asphalt, concrete) belongs to the diffuse reflection surface, and the reflected light intensity is proportional to the cosine of the angle between the light direction and the surface normal, and the phase scanning device calculates the initial micro-texture depth S ini , and the reflected light intensity directly affects the signal-to-noise ratio of the phase measurement. When the light angle increases (light inclination), the reflected light intensity decreases, the phase signal is easily disturbed by noise, and the initial micro-texture depth S ini is underestimated, and thus the reflected light intensity under different light angles is normalized to the reference of vertical incidence through the light correction factor G, ensuring that the phase difference is converted to the initial micro-texture depth S ini is not disturbed by the change of light intensity, and the initial micro-texture depth S ini is closer to the physical true value. In addition, it should be noted that when the light angle is greater than 90°, the light direction deviates from the road surface, so the data needs to be reacquired.

[0069] So far, through the derivation of the physical relationship between the optical path difference and the phase difference, and combined with the actual influence of the laser incidence angle cosθ and the light angle, the preliminary correction of the theoretical depth is realized.

[0070] Step 3: Extract the texture feature of the acquired road image data to obtain the texture density parameter, and dynamically correct the initial micro-texture depth based on the light correction factor obtained based on the light angle to obtain the corrected micro-texture depth.

[0071] The determination method of the corrected micro-texture depth in this embodiment is:

[0072] Image segmentation and edge detection are performed on the road image data, the peak point area of the road micro-texture is extracted through the threshold segmentation algorithm, and the number of peak points in a unit area is counted as the spatial density of micro-texture.

[0073] Based on the initial micro-texture depth, a correction coefficient containing the product of the micro-texture spatial density and the light correction factor is constructed, and the corrected micro-texture depth is calculated through the product of the initial micro-texture depth and the correction coefficient.

[0074] It can be understood that the initial depth map in the road surface image data has the problem of mutual restraint of "uneven texture space distribution" and "illumination interference". The texture dense area is prone to depth underestimation due to phase superposition, and insufficient light will exacerbate texture blur. The above-mentioned image plane graph image conversion technology converts the depth map into a gray scale image, and uses an edge detection algorithm to extract texture peak points, count the number of peak points N in a unit area, calculate the micro texture space density, and then optimize through image analysis (texture density extraction) and image enhancement (coupling light correction) to realize depth refinement. The calculation formula of the corrected micro texture depth after correction is as follows:

[0075] ;

[0076] In the formula:

[0077] S cor The corrected micro texture depth after correction is the spatial texture density correction, m is the micro texture space density, which is calculated by the proportion of the number of texture feature points per unit area: , and M is the area of the measurement region.

[0078] Further, in the texture dense area: when the light is sufficient (the light correction factor G is large), the texture features are clearer, the correction weight is improved, and the initial depth is prevented from being underestimated due to texture superposition.

[0079] In the texture sparse area: when the light is weak (the light correction factor G is small), the correction weight is reduced, and noise amplification caused by excessive enhancement is avoided.

[0080] In addition, the correction coefficient is dynamically adjusted in the form of "1+coupling term", so that when the texture is sparse (the micro texture space density m is 0.1) or the light is weak (the light correction factor G is 0.5), the coupling term is only 0.05, and the correction coefficient is 1.05, only slightly amplifying the initial depth, avoiding excessive correction of the flat area. When the texture is dense and the light is strong, the correction coefficient is significantly increased, ensuring that the dense texture depth is fully "restored" to achieve adaptive adaptation to different texture distribution scenes.

[0081] At this point, the product term of the micro texture space density m and the light correction factor G is introduced to compensate for the phase signal cancellation effect caused by texture density, and to improve the accuracy of depth evaluation.

[0082] Step 4: Remove false depth components through phase noise analysis to obtain the final micro texture depth.

[0083] The determination method of the final micro texture depth in the embodiment is:

[0084] Select samples in flat areas in the road surface image data, and calculate the standard deviation of the phase difference sequence as the phase measurement noise;

[0085] Divide the product of the phase measurement noise and the micro-texture spatial density by the phase difference to obtain a first proportion of the noise false depth to the corrected depth, and remove the noise false depth corresponding to the product of the first proportion and the corrected micro-texture depth from the corrected micro-texture depth to obtain the final micro-texture depth.

[0086] In specific embodiments, the noise in the texture dense area is higher, and if the noise is simply removed, the texture details will be blurred. Therefore, based on the above determination method of the final micro-texture depth, the calculation formula of the final micro-texture depth is as follows:

[0087] ;

[0088] In the formula:

[0089] S fin is the final micro-texture depth (after noise correction), and σ is the phase measurement noise;

[0090] wherein, reflects the synergistic amplification effect of noise and texture density: that is, in the texture dense area (high micro-texture spatial density m), the multi-peak reflection will exacerbate the superposition of noise, and the phase difference is smaller (the texture is shallower), the noise proportion is higher, and the false depth risk is greater.

[0091] It can be understood that the noise false depth proportion dynamically changes with the noise intensity and the texture density. Specifically, the stronger the noise and the denser the texture, the greater the proportion, and thus more false depths are removed; when the noise is weak and the texture is sparse, the proportion decreases to avoid the real texture depth from being mistakenly deleted, thereby achieving precise control of “more deletion when noise is strong, and less deletion when noise is weak”. Thus, the final micro-texture depth S fin The evaluation results are as follows:

[0092] If the final micro-texture depth S fin is greater than the initial micro-texture depth S ini , it indicates that the initial phase measurement is masked by the texture superposition or insufficient illumination, and the corrected depth is closer to the real raised height of the micro-texture, and the anti-skid performance may be higher than the initial evaluation;

[0093] If the final micro-texture depth S fin is less than the initial micro-texture depth S ini , it indicates that the initial depth contains noise or “false texture” caused by illumination, and the non-real depth is removed after correction, and the anti-skid performance may be lower than the initial evaluation;

[0094] If the final micro-texture depth Sfin equal to and approximately equal to the initial micro-texture depth S ini , it indicates that the initial phase measurement has approached the physical reality, and the final micro-texture depth S under the current iteration can be directly used without multiple iteration corrections fin , which improves the calculation efficiency.

[0095] At this point, the noise optimization processing is completed, and the final micro-texture depth S fin is obtained.

[0096] Step 5: Based on the conversion of the initial micro-texture depth based on the sequence of continuous phase differences, the relationship between the phase difference and the depth can be inversely solved, and when the initial micro-texture depth is not equal to the final micro-texture depth, the phase difference deviation value is obtained, and the corrected phase difference is obtained according to the sum of the phase difference deviation value and the phase difference to replace the phase difference in the iteration;

[0097] The root mean square error of the final micro-texture depth of the last two iterations is less than the preset threshold as the convergence condition, and the initial micro-texture depth, the corrected micro-texture depth and the final micro-texture depth are repeatedly calculated until the final micro-texture depth converges, and the final micro-texture depth is output.

[0098] In this embodiment, based on the of step 2, the relationship between the phase difference and the depth can be inversely solved:

[0099] ;

[0100] When S fin ≠ S ini , it indicates that the current phase difference has a deviation from the true phase difference, that is:

[0101] , wherein is the phase difference deviation value;

[0102] Therefore ;

[0103] ;

[0104] , wherein is the corrected phase difference.

[0105] , wherein when the final micro-texture depth S fin is output, the convergence condition that the root mean square error of the final micro-texture depth of the last two iterations is less than the preset threshold needs to be met, and if it is met, it means that the depth data has entered the “stable interval” (i.e. the depth change of subsequent iterations can be ignored), and this stability is crucial for the engineering application of highway pavement evaluation.

[0106] It can be understood that the iteration mechanism realizes adaptive optimization of depth evaluation of complex texture regions by dynamically correcting the phase difference, and ensures stability and accuracy of the evaluation results.

[0107] At this point, the final evaluation and output of the depth of the road surface micro texture are completed.

[0108] Based on the same inventive concept as the above method, the embodiments of the present application also provide a road surface micro texture depth evaluation system based on phase scanning, comprising:

[0109] The phase data acquisition module includes a laser emission unit providing a preset wavelength laser, a phase detection unit acquiring an original phase difference signal, and an environment sensing unit acquiring a laser incidence angle, an illumination angle, and road surface image data;

[0110] The initial depth calculation unit is connected with the phase data acquisition module, performs phase-depth conversion and illumination correction, and outputs an initial micro texture depth;

[0111] The texture correction unit is connected with the initial depth calculation unit, performs texture density extraction and coupling correction, and outputs a corrected micro texture depth;

[0112] The noise optimization unit is connected with the texture correction unit, performs phase noise calculation, false depth elimination, and dynamic iteration optimization, and outputs a final micro texture depth;

[0113] The result output module is connected with the noise optimization unit, generates a road surface micro texture depth visualization report and data file.

[0114] At this point, the phase data acquisition module, the initial depth calculation unit, the texture correction unit, the noise optimization unit, and the result output module in the road surface micro texture depth evaluation system based on phase scanning are connected with each other and cooperatively execute the steps of any one of the road surface micro texture depth evaluation methods based on phase scanning.

[0115] Although the present application is disclosed as above, the present application is not limited thereto. Any person skilled in the art, without departing from the spirit and scope of the present application, can make various changes and modifications, therefore the protection scope of the present application should be subject to the range defined by the claims.

Claims

1. A method for assessing the depth of micro-texture of a road surface based on phase scanning, characterized in that, The method includes the following steps: A laser with a preset wavelength is emitted to the road surface by a laser phase scanning device. The original phase difference signal of the road surface micro-texture is collected by the phase detection unit. At the same time, the laser incident angle, illumination angle and road surface image data are obtained by the environmental sensing unit. The original phase difference signal is subjected to phase unwrapping processing to eliminate phase jump interference and obtain a continuous phase difference sequence. The initial microtexture depth of the road surface is calculated based on the preset wavelength of the laser, the continuous phase difference sequence, and the laser incident angle. Texture features are extracted from the acquired road surface image data to obtain texture density parameters, and the initial micro-texture depth is dynamically corrected by combining the illumination correction factor obtained based on the illumination angle to obtain the corrected micro-texture depth. The final microtexture depth is obtained by eliminating false depth components through phase noise analysis; The convergence condition is that the root mean square error of the final microtexture depth in two consecutive iterations is less than a preset threshold. The initial microtexture depth, the corrected microtexture depth, and the final microtexture depth are repeatedly calculated until the final microtexture depth converges, and the final microtexture depth is output.

2. The phase scanning based road surface microtexture depth evaluation method according to claim 1, characterized in that, The method for determining the initial microtexture depth is as follows: A laser with a preset wavelength is emitted to the micro-textured surface of the road surface by a laser phase scanning device, and the original phase difference signal of the reflected light is collected by a phase detection unit. The original phase difference signal is then subjected to phase unwrapping processing to obtain the continuous phase difference sequence. The angle between the laser incident direction and the road surface normal is measured by an angle sensor as the laser incident angle, and the angle between the ambient light direction and the road surface normal is measured by an illumination sensor as the illumination angle. An initial depth transformation is performed on the continuous phase difference sequence: the theoretical depth under the current incident conditions is calculated based on the preset wavelength of the laser, the phase difference in the continuous phase difference sequence, and the cosine value of the laser incident angle. Then, the theoretical depth is compensated by using the cosine value of the illumination angle as an illumination correction factor, and the initial microtexture depth of the road surface microtexture is obtained.

3. The phase scanning based road surface microtexture depth evaluation method according to claim 2, characterized in that, The illumination correction factor is set and input based on the illumination angle and according to preset rules, as follows: If the illumination angle is 0°, then the illumination correction factor is set to 1; If the illumination angle is 30°, then the illumination correction factor is set to 0.866; If the illumination angle is 45°, then the illumination correction factor is set to 0.707; If the illumination angle is 60°, then the illumination correction factor is set to 0.5; If the illumination angle is 90°, then the illumination correction factor is set to 0.

4. The phase scanning based road surface microtexture depth evaluation method according to claim 2, characterized in that, The method for determining the depth of the corrected microtexture is as follows: The road surface image data is segmented and edge detected. The peak regions of the road surface micro-texture are extracted by a threshold segmentation algorithm, and the number of peaks per unit area is counted as the micro-texture spatial density. Based on the initial micro-texture depth, a correction coefficient including a product term of the micro-texture spatial density and the illumination correction factor is constructed, and the corrected micro-texture depth is calculated by multiplying the initial micro-texture depth by the correction coefficient. The correction coefficient increases with the increase of the micro-texture spatial density, so as to compensate for the phase signal cancellation problem caused by multi-peak reflection in dense texture area.

5. The phase scanning based road surface microtexture depth evaluation method according to claim 4, characterized in that, The determination method of the final micro-texture depth is as follows: In the flat area of the road surface image data, samples are selected, and the standard deviation of the phase difference sequence is calculated as the phase measurement noise. The product of the phase measurement noise and the micro-texture spatial density is divided by the phase difference to obtain a first proportion of the noise false depth to the corrected depth, and the noise false depth corresponding to the product of the first proportion and the corrected micro-texture depth is removed from the corrected micro-texture depth to obtain the final micro-texture depth.

6. The phase scanning based road surface microtexture depth evaluation method according to claim 5, characterized in that, Based on the conversion of the continuous phase difference sequence to the initial micro-texture depth, the relationship between the inverse phase difference and the depth can be solved, and when the initial micro-texture depth is not equal to the final micro-texture depth, a phase difference deviation value is obtained, and a corrected phase difference is obtained according to the sum of the phase difference deviation value and the phase difference to replace the phase difference in the iteration.

7. The phase scanning based road surface microtexture depth evaluation method according to claim 3, wherein, If the input of the illumination correction factor is 0, it is considered as invalid illumination, and the data needs to be re-collected.

8. A road surface microtexture depth evaluation system for performing the phase scanning-based road surface microtexture depth evaluation method according to any one of claims 1 to 7, characterized by, It includes: A phase data acquisition module: including a laser emission unit providing a preset wavelength laser, a phase detection unit collecting original phase difference signals, and an environment sensing unit acquiring laser incidence angle, illumination angle and road surface image data; An initial depth calculation unit: connected with the phase data acquisition module, performing phase-depth conversion and illumination correction, and outputting initial micro-texture depth; A texture correction unit: connected with the initial depth calculation unit, performing texture density extraction and coupling correction, and outputting corrected micro-texture depth; A noise optimization unit: connected with the texture correction unit, performing phase noise calculation, false depth removal and dynamic iteration optimization, and outputting final micro-texture depth; A result output module: connected with the noise optimization unit, generating road micro-texture depth visualization report and data file. A result output module: connected with the noise optimization unit, generating road micro-texture depth visualization report and data file.

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