Method and apparatus for detecting type of crack disease in asphalt pavement, electronic device, and medium

Abstract

A method and apparatus for detecting the type of a crack disease in an asphalt pavement, an electronic device, and a medium, relating to the technical field of road engineering. The method comprises: acquiring electromagnetic detection data of an asphalt pavement to be detected; determining the position of a hidden crack road section on the basis of the electromagnetic detection data; acquiring deflection detection data at the position of the hidden crack road section, and on the basis of the deflection detection data, determining a deflection basin index of the hidden crack road section; and setting a deflection ratio threshold, and on the basis of the deflection basin index and the deflection ratio threshold, determining the disease type of the hidden crack road section. The position of the hidden crack road section is determined by means of the electromagnetic detection data, and by means of comparison of the deflection ratio threshold and the deflection basin index, the hidden crack disease type of a hidden crack in an asphalt pavement is accurately determined by quantitatively analyzing the deflection basin index of the hidden crack road section.

Description

Asphalt pavement crack disease type detection method and device, electronic equipment and medium TECHNICAL FIELD

[0001] The present application relates to the technical field of road engineering, in particular to an asphalt pavement crack disease type detection method, device, electronic equipment and medium. BACKGROUND

[0002] Ground penetrating radar and falling weight deflectometer are two commonly used detection methods for pavement detection. Ground penetrating radar is based on the penetration and transmission stability of high-frequency electromagnetic waves. High-frequency electromagnetic waves are emitted to the pavement, and the received reflected waves are converted into electrical signals for analysis. The falling weight deflectometer is a device that detects pavement deformation by using a deflectometer to drop from a certain height, applies a pulse load to the pavement through the impact of the bearing plate, and detects the deformation of the pavement at different distances using sensors. The computer generates deflection data and deflection basin shape under dynamic load. The prior art uses these two methods to complement each other to detect pavement cracks. It can quantify the internal conditions of crack diseases in asphalt pavement and characterize the structural performance of asphalt crack disease sections.

[0003] However, in the existing method for detecting asphalt crack diseases, it is difficult to determine the type of crack disease. The prior art usually observes and determines whether the crack disease is a horizontal crack or a vertical crack based on experience. Hidden cracks hidden inside the pavement cannot be determined as to the type of crack disease, and the appropriate road maintenance strategy cannot be selected according to the type of crack in subsequent road maintenance.

[0004] Therefore, the prior art has the technical problem of being unable to determine the type of hidden crack disease in asphalt pavement crack disease detection.

[0005] SUMMARY

[0006] Therefore, it is necessary to provide an asphalt pavement crack disease type detection method, device, electronic equipment and computer readable storage medium to solve the technical problem of the prior art that it is difficult to determine the type of hidden crack disease.

[0007] To solve the above problems, on the one hand, the present application provides an asphalt pavement crack disease type detection method, comprising:

[0008] Obtaining electromagnetic detection data of the asphalt pavement to be detected;

[0009] Determining the location of the hidden crack section according to the electromagnetic detection data;

[0010] Obtaining deflection detection data at the location of the hidden crack section, and determining a deflection basin index of the hidden crack section according to the deflection detection data;

[0011] A deflection ratio threshold is set, and the hidden crack road section disease type is determined according to the deflection basin index and the deflection ratio threshold.

[0012] In a possible implementation, the hidden crack road section position is determined according to the electromagnetic detection data, and the method comprises the following steps:

[0013] The electromagnetic detection data is subjected to contrast adjustment and pile number correction to obtain a radar detection image and pile number information;

[0014] The crack disease position is determined according to the radar detection image and the pile number information.

[0015] In a possible implementation, the asphalt pavement crack disease type detection method further comprises quantitative analysis of the asphalt pavement crack disease, and the quantitative analysis of the asphalt pavement crack disease comprises the following steps:

[0016] The radar detection image is subjected to contrast adjustment to obtain an adjusted radar image;

[0017] The staggered layer stripes in the adjusted radar image are determined, and the crack quantitative index of the asphalt pavement to be detected is determined according to the staggered layer stripes and the pile number information;

[0018] The crack quantitative index comprises a radar image crack degree.

[0019] In a possible implementation, the deflection detection data of the hidden crack road section comprises actual displacement values measured by each sensor of a falling weight deflectometer and horizontal distances of each sensor from a center sensor measuring point of the falling weight.

[0020] In a possible implementation, the deflection basin index of the hidden crack road section is determined according to the deflection detection data, and the method comprises the following steps:

[0021] The direct deflection index, the deflection difference ratio index, the rate index and the area index of the hidden crack road section are calculated according to the deflection detection data.

[0022] In a possible implementation, the deflection difference ratio index comprises a 0-1 deflection ratio, a deflection ratio threshold is set, and the hidden crack road section disease type is determined according to the deflection basin index and the deflection ratio threshold, and the method comprises the following steps:

[0023] The 0-1 deflection ratio is compared with the deflection ratio threshold, the hidden crack road section with a 0-1 deflection ratio lower than the deflection ratio threshold is divided into a longitudinal crack type, and the hidden crack road section with a 0-1 deflection ratio higher than the deflection ratio threshold is divided into a transverse crack type.

[0024] In a possible implementation, the deflection basin index of the hidden crack road section is determined according to the deflection detection data, and the method further comprises the following steps:

[0025] Based on a pavement structure layer mechanical model, modulus values of the pavement structure layer are obtained by inversely calculating the modulus of the pavement structure layer according to the deflection basin data.

[0026] In another aspect, the present application also provides an asphalt pavement crack disease type detection device, comprising:

[0027] An electromagnetic detection unit is configured to acquire electromagnetic detection data of the asphalt pavement to be detected.

[0028] A crack section determination unit is configured to determine the position of the hidden crack section according to the electromagnetic detection data.

[0029] A deflection basin index establishment unit is configured to acquire deflection detection data at the position of the hidden crack section, and determine the deflection basin index of the hidden crack section according to the deflection detection data.

[0030] A crack type judgment unit is configured to set a deflection ratio threshold, and determine the disease type of the hidden crack section according to the deflection basin index and the deflection ratio threshold.

[0031] In another aspect, the present application also provides an electronic device, comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, wherein the processor implements the asphalt pavement crack disease type detection method when executing the program.

[0032] In another aspect, the present application also provides a computer readable storage medium, which stores a computer program, wherein the computer program is executable on the processor to implement the asphalt pavement crack disease type detection method.

[0033] The present application has the following beneficial effects: the present application determines the position of the hidden crack section according to the electromagnetic detection data, and compares the deflection ratio threshold and the deflection basin index, and quantitatively analyzes the deflection basin index of the hidden crack section to accurately determine the hidden crack disease type of the hidden crack section. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0035] FIG. 1 is a flowchart of an embodiment of the asphalt pavement crack disease type detection method provided by the present application;

[0036] FIG. 2 is a flowchart of determining the position of the hidden crack section according to an embodiment of the present application;

[0037] Fig. 3 is a structural schematic diagram of an embodiment of the asphalt pavement crack disease type detection device provided by the present application;

[0038] Fig. 4 is a structural schematic diagram of an embodiment of the electronic device provided by the present application. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person skilled in the art without creative work fall within the protection scope of the present application.

[0040] It should be understood that the accompanying drawings of the schematic diagram are not drawn according to the actual proportions. The flowchart used in the present application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can not be implemented in sequence, and the steps without logical context relationship can be reversed in sequence or implemented simultaneously. In addition, a person skilled in the art can add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of the present application.

[0041] Some block diagrams shown in the accompanying drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor systems and / or microcontroller systems.

[0042] In this document, the phrase "embodiment" means that the specific features, structures or characteristics described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor is it an independent or alternative embodiment to other embodiments. A person skilled in the art explicitly and implicitly understands that the embodiments described herein can be combined with other embodiments.

[0043] Fig. 1 is a flowchart of an embodiment of the asphalt pavement crack disease type detection method provided by the present application, as shown in Fig. 1, the asphalt pavement crack disease type detection method comprises:

[0044] S101, acquiring electromagnetic detection data of a to-be-detected asphalt pavement;

[0045] S102, determining a hidden crack section position according to the electromagnetic detection data;

[0046] S103, obtain deflection detection data at the position of the hidden crack section, and determine a deflection basin index of the hidden crack section according to the deflection detection data;

[0047] S104, set a deflection ratio threshold, and determine a disease type of the hidden crack section according to the deflection basin index and the deflection ratio threshold.

[0048] Compared with the prior art, the asphalt pavement crack disease type detection method provided by the present application determines the position of the hidden crack section through electromagnetic detection data, and compares the deflection ratio threshold and the deflection basin index, and quantitatively analyzes the deflection basin index of the hidden crack section to accurately determine the hidden crack disease type of the hidden crack of the asphalt pavement.

[0049] In some embodiments of the present application, the position of the hidden crack section is determined according to the electromagnetic detection data, as shown in FIG. 2, which includes:

[0050] S201, contrast adjustment and stake number correction are performed on the electromagnetic detection data to obtain a radar detection image and stake number information;

[0051] S202, determine a crack disease position according to the radar detection image and the stake number information.

[0052] Specifically, in the embodiment, the ground penetrating radar identifies and extracts the thickness of the pavement structure layer by utilizing the different dielectric characteristics of different layers. The process of extracting the thickness of the pavement structure layer can be summarized as two parts: preparation and identification. The preparation first imports the electromagnetic detection data, including the camera shooting record during radar detection and the original signal data of electromagnetic wave detection. Then, the dielectric constant display interval is set, and the contrast is adjusted to obtain a clear radar detection image. Before each small section of pavement detection starts, the mileage stake number on the guardrail at the starting point is read, and the initial mileage is recorded to complete the mileage calibration to determine the position corresponding to each part of the detection data, i.e. the stake number information. The mileage calibration formula is as follows: R0±(S x -S0)=R x

[0053] Wherein, R0 represents the actual stake number at the starting point, S x represents the stake number of the observation point at the lower left corner of the photo area, S0 represents the stake number of the starting point at the lower left corner of the photo area, R x represents the actual stake number of the observation point.

[0054] In some embodiments of the present application, the asphalt pavement crack disease type detection method further includes quantitatively analyzing the asphalt pavement crack disease, and the quantitatively analyzing the asphalt pavement crack disease includes:

[0055] contrast adjustment is performed on the radar detection image to obtain an adjusted radar image;

[0056] determining the adjustment of the false layer stripe in the radar image, determining the crack quantitative index of the asphalt pavement to be detected according to the false layer stripe and the stake information;

[0057] The crack quantitative index includes a radar image crack degree.

[0058] Specifically, to provide data support for the analysis of hidden cracks, in addition to determining the location of the hidden crack section through the electromagnetic detection data, the embodiment also establishes a quantitative index of the hidden crack. First, for the radar detection image obtained in the previous step, the vertical false layer stripe can be seen, but the clarity of the false layer stripe along the depth direction is not high, so it is necessary to adjust the contrast again until the vertical false layer stripe is clearly displayed. To ensure the comparability of the crack diseases of different sections, the embodiment defines the depth of the vertical false layer stripe in the radar detection image as the crack image influence depth di.

[0059] To accurately extract the depth of the vertical false layer stripe, i.e., the crack image influence depth, the embodiment uses the order in which the peak voltage reaches in the electric signal image area to determine the position of the vertical stripe. Compared with the non-stripe position, the voltage peak value at the vertical stripe position will appear obvious lag.

[0060] After the vertical false layer stripe is determined, the embodiment calculates the pavement thickness according to the electric signal parameters in the radar detection image, and outputs the pavement distance from the bottom end of the vertical false layer stripe, i.e., the crack image influence depth d i , and according to the corresponding stake information x i , obtains the radar image crack degree P, which is expressed as follows:

[0061] Wherein, x i represents the stake number at the vertical false layer stripe in the radar detection image, d i represents the crack image influence depth at each vertical false layer stripe, n represents the number of vertical false layer stripes, represents the average value of the stake numbers participating in the calculation, and s represents the standard deviation of the stake number distribution participating in the calculation, represents the average value of the crack image influence depth, and P represents the radar image crack degree.

[0062] In some embodiments of the present application, the deflection detection data of the hidden crack section includes the actual displacement values measured by each sensor of the falling weight deflectometer and the horizontal distances of each sensor from the center sensor of the falling weight.

[0063] In some embodiments of the present application, the deflection basin index of the hidden crack section is determined according to the deflection detection data, including:

[0064] The direct deflection index, the deflection difference ratio index, the rate index and the area index of the hidden crack road section are calculated according to the deflection detection data.

[0065] Specifically, the deflection detection data in the embodiment is the actual displacement value D i measured by each sensor in the test process of the falling weight deflectometer i and the horizontal distance R of each sensor from the center sensor point of the falling weight.

[0066] The direct deflection index is the actual displacement value D i measured by the sensor on the falling weight deflectometer, which is generally used to calculate the deflection value at the center of the falling weight.

[0067] The deflection difference ratio index includes the deflection difference and the deflection ratio. The deflection difference is the difference between the deflection value at the center sensor point and the deflection value at any sensor point, and the deflection ratio is the ratio of the deflection value at any sensor point to the deflection value at the center sensor point. The ratio of the deflection value at the center sensor point to the deflection value measured by the first adjacent sensor has a strong correlation with the modulus of the pavement structure layer at the sensor point, so the embodiment uses it as an index to judge the disease type of the hidden crack road section. The calculation formulas of the deflection difference and the deflection ratio are as follows:

[0068] Wherein, represents the deflection difference, i = 1, 2,..., 6, represents the deflection ratio, i = 1, 2,..., 6, D0 represents the sensor displacement value at the center of the falling weight, and D i represents the sensor displacement value at any sensor point

[0069] The rate index includes the slope index, the curvature index and the shape index, and the calculation results are related to the selected curve fitting model. These indexes mainly reflect the inclination degree, the roundness degree and the concave-convex property of the deflection basin curve. The formulas are as follows:

[0070] Wherein, F i represents the shape index, i = 1, 2,..., 5, C h represents the curvature index, i = 1, 2,..., 5, S i represents the slope index, R i represents the horizontal distance of the i-th sensor point from the center sensor point of the falling weight.

[0071] The area index refers to the area of a curved trapezoid formed by the ground level line and the deflection basin curve, and is a good energy representation. Under the elastic or viscoelastic theory, the area index represents the total strain energy after the impact load, and is expressed as follows:

[0072] wherein A represents the area index.

[0073] In some embodiments of the present application, the deflection difference ratio index includes a 0-1 deflection ratio, a deflection ratio threshold is set, and the hidden crack road section disease type is determined according to the deflection basin index and the deflection ratio threshold, including:

[0074] The 0-1 deflection ratio is compared with the deflection ratio threshold, and the hidden crack road section with a 0-1 deflection ratio lower than the deflection ratio threshold is divided into a longitudinal crack type, and the hidden crack road section with a 0-1 deflection ratio higher than the deflection ratio threshold is divided into a transverse crack type.

[0075] Specifically, the 0-1 deflection ratio refers to the ratio of the deflection value of the drop hammer type deflectometer at the drop hammer point to the deflection value of the nearest sensor No. 1 bit measuring point. Research shows that different crack types have different performances in the values of the 0-1 deflection ratio and the 0-1 deflection difference. For the measured asphalt pavement type in the embodiments, the 0-1 deflection ratio of the transverse crack is generally located at 1.4-2.1, the 0-1 deflection difference is generally greater than 50 μm, and the 0-1 deflection ratio of the longitudinal crack is generally located at 1.2-1.4, and the 0-1 deflection difference is generally in the range of 20-40 μm. Therefore, the embodiments take 1.4 as the deflection ratio threshold, compare the deflection ratio of the hidden crack road section with the deflection ratio threshold, divide the hidden crack road section with a deflection ratio lower than the deflection ratio threshold into a longitudinal crack type, and divide the hidden crack road section with a deflection ratio higher than the deflection ratio threshold into a transverse crack type, so as to determine the main crack type of the hidden crack road section.

[0076] In some embodiments of the present application, the deflection basin index of the hidden crack road section is determined according to the deflection detection data, and further includes:

[0077] Based on the pavement structure layer mechanical model, the modulus value of the pavement structure layer is obtained by inversely calculating the deflection basin data.

[0078] Specifically, the embodiments further provide an inversely calculated modulus value of the pavement structure layer, and provide data support for the analysis of hidden cracks. The embodiments establish a pavement structure layer mechanical model, calculate the deflection value according to the pavement structure layer mechanical model, compare the calculated deflection value with the measured deflection value, iterate until the deflection error meets the accuracy requirement, and output the inverse calculation result.

[0079] In summary, the present application determines the location of the hidden crack section through electromagnetic detection data, compares the deflection ratio threshold and the deflection basin index, and accurately judges the hidden crack disease type of the asphalt pavement hidden crack section through quantitative analysis of the deflection basin index of the hidden crack section.

[0080] Based on the asphalt pavement crack disease type detection provided by the present application, the present application further provides an asphalt pavement crack disease type detection device, as shown in FIG. 3, an asphalt pavement crack disease type detection device 300, comprising:

[0081] The electromagnetic detection unit 301 is used to obtain the electromagnetic detection data of the asphalt pavement to be detected.

[0082] The crack section determination unit 302 is used to determine the location of the hidden crack section according to the electromagnetic detection data.

[0083] The deflection basin index establishment unit 303 is used to obtain the deflection detection data at the location of the hidden crack section, and determine the deflection basin index of the hidden crack section according to the deflection detection data.

[0084] The crack type judgment unit 304 is used to set a deflection ratio threshold, and determine the disease type of the hidden crack section according to the deflection basin index and the deflection ratio threshold.

[0085] The asphalt pavement crack disease type detection device 300 provided by the above-mentioned embodiments can realize the technical solutions in the asphalt pavement crack disease type detection method embodiments, and the principles of the specific implementation of the above-mentioned modules or units can be referred to the corresponding content in the asphalt pavement crack disease type detection method embodiments, which will not be described here.

[0086] The present application further provides an electronic device 400, as shown in FIG. 4, which is a structural schematic diagram of an embodiment of the electronic device provided by the present application, the electronic device 400 comprising a processor 401, a memory 402, and a computer program stored in the memory 402 and executable on the processor 401, wherein the processor 401 executes the program to realize the asphalt pavement crack disease type detection method.

[0087] As a preferred embodiment, the above-mentioned electronic device further comprises a display 403 for displaying the process of the processor 401 executing the above-mentioned asphalt pavement crack disease type detection method.

[0088] The processor 401 can be an integrated circuit chip with a processing capability of signals. The processor 401 can be a general processor, including a central processing unit (CPU), a network processor (NP), etc., and can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), etc. The processor 401 can implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application. The general processor can also be a microprocessor or any conventional processor.

[0089] The memory 402 can be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a secure digital (SD card), a flash card, etc. The memory 402 is used to store programs, and the processor 401 executes the programs after receiving an execution instruction. The method defined by the flow disclosed in any of the embodiments of the present application can be applied to the processor 401 or implemented by the processor 401.

[0090] The display 403 can be an LED display screen, a liquid crystal display or a touch display, etc. The display 403 is used to display various information of the electronic device 400.

[0091] It can be understood that the structure shown in FIG. 4 is only a structure schematic diagram of the electronic device 400, and the electronic device 400 can further include more or fewer components than those shown in FIG. 4. The components shown in FIG. 4 can be realized by hardware, software or a combination thereof.

[0092] The embodiments of the present application further provide a computer readable storage medium, which stores a computer program. The program is executed by a processor to implement the software requirement error detection network training method and / or the software requirement error detection network application method.

[0093] Generally, the computer instructions for implementing the method of the present application can be carried by any combination of one or more computer readable storage media. The non-transitory computer readable storage medium can include any computer readable medium except the signal itself in the transitory transmission.

[0094] Computer readable storage media, for example, can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer readable storage media include: electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, computer readable storage media can be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0095] The foregoing is considered as illustrative only of the principles of the application. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the application to the exact construction and practice described. Accordingly, all such variations are intended to be included within the scope of present application as defined in the following claims.

Claims

1. An asphalt pavement crack disease type detection method, characterized by, The method comprises the following steps: acquiring electromagnetic detection data of an asphalt pavement to be detected; determining a hidden crack section position according to the electromagnetic detection data; acquiring deflection detection data at the hidden crack section position, and determining a deflection basin index of the hidden crack section according to the deflection detection data; setting a deflection ratio threshold, and determining a hidden crack section disease type according to the deflection basin index and the deflection ratio threshold.

2. The method of claim 1, wherein the method is characterized by, The step of determining the hidden crack section position according to the electromagnetic detection data comprises the following steps: adjusting contrast and correcting a stake number of the electromagnetic detection data to obtain a radar detection image and stake number information; determining a crack disease position according to the radar detection image and the stake number information.

3. The method of claim 2, wherein the method further comprises: The method further comprises quantitatively analyzing the asphalt pavement crack disease, and the step of quantitatively analyzing the asphalt pavement crack disease comprises the following steps: adjusting contrast of the radar detection image to obtain an adjusted radar image; determining a staggered layer stripe in the adjusted radar image, and determining a crack quantitative index of the asphalt pavement to be detected according to the staggered layer stripe and the stake number information; wherein the crack quantitative index comprises a radar image crack degree.

4. The method of claim 1, wherein the method is characterized by, The deflection detection data of the hidden crack section comprises actual displacement values measured by each sensor of a falling weight deflectometer and horizontal distances of each sensor from a center sensor measuring point of the falling weight.

5. The method of claim 1, wherein the method further comprises: The step of determining the deflection basin index of the hidden crack section according to the deflection detection data comprises the following steps: calculating a direct deflection index, a deflection difference ratio index, a rate index and an area index of the hidden crack section according to the deflection detection data. The deflection difference ratio index comprises a 0-1 deflection ratio, and the step of setting the deflection ratio threshold and determining the hidden crack section disease type according to the deflection basin index and the deflection ratio threshold comprises the following steps:

6. The method of claim 5, wherein the method further comprises: comparing the 0-1 deflection ratio with the deflection ratio threshold, and dividing hidden crack sections with the 0-1 deflection ratio lower than the deflection ratio threshold into a longitudinal crack type and dividing hidden crack sections with the 0-1 deflection ratio higher than the deflection ratio threshold into a transverse crack type. The step of determining the deflection basin index of the hidden crack section according to the deflection detection data further comprises the following step:

7. The method of claim 5, wherein the method further comprises: determining a type of the crack defect of the asphalt pavement based on the crack defect information. performing pavement structure layer modulus back calculation based on a pavement structure layer mechanics model according to the deflection basin data to obtain a modulus value of the pavement structure layer. The method comprises the following steps:

8. A device for detecting the type of cracks in asphalt pavement, characterized in that: an electromagnetic detection unit configured to acquire electromagnetic detection data of an asphalt pavement to be detected; a crack section determination unit configured to determine a hidden crack section position according to the electromagnetic detection data; a deflection basin index establishment unit configured to acquire deflection detection data at the hidden crack section position, and determine a deflection basin index of the hidden crack section according to the deflection detection data; a crack type judgment unit configured to set a deflection ratio threshold, and determine a hidden crack section disease type according to the deflection basin index and the deflection ratio threshold. A processor executes a program to implement the asphalt pavement crack disease type detection method according to any one of claims 1 to 7.

9. An electronic device comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program is executed by a processor to implement the asphalt pavement crack disease type detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, ​

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