Culvert detection method and device

Abstract

The invention relates to the technical field of image processing, and provides a culvert detection method and device. The method comprises the steps of inputting an inner wall image into a first detection model for image detection when the rainfall information meets the preset rainfall information according to a matching result of the rainfall information of an area where a culvert is located and the preset rainfall information, or inputting the inner wall image into a second detection model for image detection when the rainfall information does not meet the preset rainfall information; and inputting the inner wall image into a second detection model larger than the first detection model for image detection to obtain a detection result of the culvert. According to the culvert detection method provided by the embodiment of the invention, the culvert detection efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a culvert detection method and device. Background Art

[0002] A culvert is a small drainage structure built under the roadbed and is one of the components of the drainage system. In order to improve the safety of the drainage system, it is usually necessary to detect whether the culvert has defects, such as cracks.

[0003] In the related art, the method for detecting whether a culvert has defects is usually to collect images of the inner wall of the culvert and input them into a trained image detection model to determine whether the culvert has defects.

[0004] In order to improve the accuracy of culvert defect detection, the related technology uses a lightweight image detection model to preliminarily screen the inner wall image of the culvert, and then inputs the preliminarily screened inner wall image into a complex image detection model for further verification to determine whether the culvert has defects. However, this detection method may require the use of two detection models to detect the inner wall image, resulting in a large amount of computing resources being occupied, a slow detection speed, and affecting the detection efficiency of the culvert. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems existing in the related art. To this end, the present application proposes a culvert detection method, which can improve the detection efficiency of the culvert.

[0006] The present application also proposes a culvert detection device.

[0007] The application also provides an electronic device.

[0008] The present application also proposes a computer-readable storage medium.

[0009] According to the first aspect of the present application, a culvert detection method includes: Get rainfall information in the area where the culvert is located; According to the matching result between the rainfall information and the preset rainfall information, at least one frame of the inner wall image of the culvert acquired within the preset time interval is input into the first detection model or the second detection model for image detection to obtain the detection result of the culvert; The preset time interval is the time interval between the moment of obtaining the rainfall information this time and the moment of obtaining the rainfall information next time; The matching result is that the rainfall information meets the preset rainfall information, and the inner wall image is input into the first detection model for image detection; or the matching result is that the rainfall information does not meet the preset rainfall information, and the inner wall image is input into the second detection model for image detection, and the first detection model is smaller than the second detection model.

[0010] By matching the rainfall information of the area where the culvert is located with the preset rainfall information, when the rainfall information meets the preset rainfall information, the inner wall image is input into the first detection model for image detection, or when the rainfall information does not meet the preset rainfall information, the inner wall image is input into the second detection model that is larger than the first detection model for image detection, so as to obtain the detection result of the culvert, thereby minimizing the accuracy of the detection of the inner wall of the culvert, reducing the resource occupation of the detection of the inner wall of the culvert, and improving the detection efficiency of the culvert.

[0011] According to an embodiment of the present application, according to the matching result between the rainfall information and the preset rainfall information, at least one frame of the inner wall image of the culvert acquired within a preset time interval is input into the first detection model or the second detection model for image detection to obtain the detection result of the culvert, including: Determining that the rainfall information satisfies preset rainfall information, and acquiring the inner wall images of the culvert according to a target acquisition frequency greater than a preset acquisition frequency within the preset time interval; Each of the inner wall images is input into a first detection model for image detection to obtain a detection result of the culvert.

[0012] According to an embodiment of the present application, each of the inner wall images is input into a first detection model for image detection to obtain a detection result of the culvert, including: Inputting any target image among the inner wall images into the first detection model for image detection to obtain a detection result of the target image; Determine that the detection result of the target image is that there is no inner wall defect, and the detection result obtained by inputting the inner wall image of the previous frame of the target image into the first detection model is that there is no inner wall defect, and determine that the detection result of the culvert at the target moment of obtaining the target image is that there is no inner wall defect.

[0013] According to an embodiment of the present application, each of the inner wall images is input into a first detection model for image detection to obtain a detection result of the culvert, including: Inputting any target image among the inner wall images into the first detection model for image detection to obtain a detection result of the target image; Determine that the detection result of the target image is that there is an inner wall defect, and the detection result obtained by inputting the inner wall image of the next frame of the target image into the first detection model is that there is an inner wall defect, and determine that the detection result of the culvert at the target moment of obtaining the target image is that there is an inner wall defect.

[0014] According to an embodiment of the present application, according to the matching result between the rainfall information and the preset rainfall information, at least one frame of the inner wall image of the culvert acquired within a preset time interval is input into the first detection model or the second detection model for image detection to obtain the detection result of the culvert, including: Determining that the rainfall information does not meet the preset rainfall information, acquiring the inner wall images of the culvert according to the preset acquisition frequency within the preset time interval; Each of the inner wall images is input into a second detection model for image detection to obtain a detection result of the culvert.

[0015] According to one embodiment of the present application, it also includes: When the rainfall amount in the rainfall information reaches the preset rainfall amount in the preset rainfall information, or the rainfall speed in the rainfall information reaches the preset rainfall speed in the preset rainfall information, it is determined that the rainfall information meets the preset rainfall information.

[0016] According to one embodiment of the present application, it also includes: If the rainfall amount in the rainfall information is less than the preset rainfall amount in the preset rainfall information, and the rainfall speed in the rainfall information is less than the preset rainfall speed in the preset rainfall information, it is determined that the rainfall information does not meet the preset rainfall information.

[0017] A culvert detection device according to the second aspect of the present application includes: An information acquisition module is used to obtain rainfall information in the area where the culvert is located; A culvert detection module, for inputting at least one frame of inner wall image of the culvert acquired within a preset time interval into a first detection model or a second detection model for image detection according to a matching result between the rainfall information and preset rainfall information, so as to obtain a detection result of the culvert; The preset time interval is the time interval between the moment of obtaining the rainfall information this time and the moment of obtaining the rainfall information next time; The matching result is that the rainfall information meets the preset rainfall information, and the inner wall image is input into the first detection model for image detection; or the matching result is that the rainfall information does not meet the preset rainfall information, and the inner wall image is input into the second detection model for image detection, and the first detection model is smaller than the second detection model.

[0018] According to an electronic device of an embodiment of the third aspect of the present application, the electronic device includes a processor and a memory storing a computer program, and when the processor executes the computer program, the culvert detection method described in any of the above embodiments is implemented.

[0019] According to the computer-readable storage medium of the fourth aspect of the present application, a computer program is stored thereon, and when the computer program is executed by a processor, the culvert detection method described in any of the above embodiments is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A flow chart of a culvert detection method according to some embodiments of the present application; Figure 2 A schematic diagram of the structure of a culvert detection device provided in some embodiments of the present application; Figure 3 A schematic diagram of the structure of an electronic device provided for some embodiments of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] Below, the culvert detection method and device provided in the embodiments of the present application will be introduced and explained in detail through several specific embodiments.

[0024] In one embodiment, a culvert detection method is provided, which is applied to a terminal device for detecting abnormalities in a culvert. The terminal device may be a desktop terminal, a mobile terminal or a server, and the server may be an independent server or a server cluster composed of multiple servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud message databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and large message data and artificial intelligence sampling point devices.

[0025] like Figure 1 As shown, a culvert detection method provided in this embodiment includes: Step 101, obtaining rainfall information of the area where the culvert is located; Step 102, according to the matching result between the rainfall information and the preset rainfall information, at least one frame of the inner wall image of the culvert acquired within a preset time interval is input into the first detection model or the second detection model for image detection to obtain the detection result of the culvert; The preset time interval is the time interval between the moment of obtaining the rainfall information this time and the moment of obtaining the rainfall information next time; The matching result is that the rainfall information meets the preset rainfall information, and the inner wall image is input into the first detection model for image detection; or the matching result is that the rainfall information does not meet the preset rainfall information, and the inner wall image is input into the second detection model for image detection, and the first detection model is smaller than the second detection model.

[0026] In some embodiments, when it is necessary to detect the culvert, rainfall information of the area where the culvert is located can be obtained at a preset time interval. The preset time interval can be set according to actual conditions. For example, rainfall information of the area where the culvert is located is obtained every 10 minutes. The rainfall information may include at least one of rainfall amount or rainfall speed, which can be obtained through weather forecast or rainfall sensor detection.

[0027] After obtaining the rainfall information of the area where the culvert is located, the rainfall information can be compared with the preset rainfall information of the same information type. If the rainfall information is rainfall amount, the preset rainfall information is the preset rainfall amount; if the rainfall information is rainfall speed, the preset rainfall information is the preset rainfall speed. Among them, the preset rainfall information can be set according to the actual situation, such as the critical value that causes leakage of the defective inner wall of the culvert. Exemplarily, if the rainfall information is rainfall speed, the critical rainfall that causes leakage of the defective inner wall of the culvert can be determined as the preset rainfall. The critical rainfall indicates that if the rainfall reaches the critical rainfall, it is likely to cause leakage of the defective inner wall of the culvert.

[0028] If the rainfall information meets the preset rainfall information, such as the rainfall reaches the preset rainfall amount, if there are defects on the inner wall of the culvert that cause leakage, the defects on the inner wall of the culvert will most likely cause leakage in the culvert. At this time, it is easier to identify the inner wall defects that cause leakage through the inner wall image. Therefore, within a preset time interval between the moment when the rainfall information is obtained this time and the moment when the rainfall information is obtained next time, at least the inner wall image of the inner wall of the culvert can be obtained through a camera device, and the obtained inner wall image can be input into the trained first detection model for image detection.

[0029] The first detection model is a lightweight image detection model, such as a single-stage detection model such as YOLO. The first detection model can be trained by taking each inner wall image with inner wall defects as a negative sample and each inner wall image without inner wall defects as a positive sample, and then inputting each inner wall image sample composed of positive and negative samples into the first detection model for training to obtain a trained first detection model.

[0030] After obtaining the trained first detection model, if the rainfall information in the area where the culvert is located meets the preset rainfall information, it can be determined that if there is an inner wall defect on the inner wall of the culvert, the inner wall defect is likely to cause leakage in the culvert, and it is easier to identify the inner wall defect that causes the leakage through the inner wall image. Therefore, the inner wall image collected within the preset time interval can be input into the trained first detection model for image detection to obtain the detection result output by the first detection model. If the detection result input by the first detection model is that the inner wall image has an inner wall defect, it can be determined that the detection result of the culvert is that there is an inner wall defect. Otherwise, it can be determined that the detection result of the culvert is that there is no inner wall defect. In this way, while the accuracy of the detection of the inner wall of the culvert is improved as much as possible, the resource occupation of the detection of the inner wall of the culvert is reduced.

[0031] If the rainfall information does not meet the preset rainfall information, such as the rainfall is less than the preset rainfall, if there is an inner wall defect on the inner wall of the culvert that causes leakage, it is highly unlikely to cause leakage in the culvert. Since the first detection model is a lightweight model, it is difficult to use the first detection model to identify the inner wall image to find the subtle inner wall defects that may cause leakage. More sophisticated image detection is required to identify the inner wall defects. Therefore, within a preset time interval between the moment when the rainfall information is obtained this time and the moment when the rainfall information is obtained next time, at least the inner wall image of the inner wall of the culvert can be obtained through a camera device, and the obtained inner wall image can be input into the trained second detection model for image detection.

[0032] Among them, the second detection model is a non-lightweight image detection model, such as a multi-stage detection model such as R-CNN. The training of the second detection model can also be carried out by taking each inner wall image with inner wall defects as a negative sample and each inner wall image without inner wall defects as a positive sample, and then inputting each inner wall image sample composed of positive and negative samples into the second detection model for training, so as to obtain a trained second detection model.

[0033] After obtaining the trained second detection model, if the rainfall information in the area where the culvert is located does not meet the preset rainfall information, it can be determined that even if there is an inner wall defect on the inner wall of the culvert, it is difficult to identify the inner wall defect that causes the leakage from the inner wall image through the lightweight model. Therefore, the inner wall image collected within the preset time interval can be input into the trained second detection model for image detection to obtain the detection result output by the second detection model. If the detection result input by the second detection model is that there is an inner wall defect in the inner wall image, it can be determined that the detection result of the culvert is that there is an inner wall defect. Otherwise, it can be determined that the detection result of the culvert is that there is no inner wall defect. Therefore, the inner wall image that is difficult to identify is directly detected by the second detection model without the need for preliminary screening by the lightweight first detection model, so as to maximize the accuracy of the detection of the inner wall of the culvert while reducing the resource occupation of the detection of the inner wall of the culvert.

[0034] By matching the rainfall information of the area where the culvert is located with the preset rainfall information, when the rainfall information meets the preset rainfall information, the inner wall image is input into the first detection model for image detection, or when the rainfall information does not meet the preset rainfall information, the inner wall image is input into the second detection model that is larger than the first detection model for image detection, so as to obtain the detection result of the culvert, thereby minimizing the accuracy of the detection of the inner wall of the culvert, reducing the resource occupation of the detection of the inner wall of the culvert, and improving the detection efficiency of the culvert.

[0035] In order to improve the timeliness of culvert detection, in some embodiments, according to the matching result between the rainfall information and the preset rainfall information, at least one frame of the inner wall image of the culvert acquired within a preset time interval is input into the first detection model or the second detection model for image detection to obtain the detection result of the culvert, including: Determining that the rainfall information satisfies preset rainfall information, and acquiring the inner wall images of the culvert according to a target acquisition frequency greater than a preset acquisition frequency within the preset time interval; Each of the inner wall images is input into a first detection model for image detection to obtain a detection result of the culvert.

[0036] In some embodiments, if the rainfall information in the area where the culvert is located meets the preset rainfall information, such as the rainfall reaches the preset rainfall, if there are defects on the inner wall of the culvert that cause leakage, the defects on the inner wall of the culvert are likely to cause leakage in the culvert, so the timeliness of culvert detection is required to be higher. Based on this, within the preset time interval, a target acquisition frequency greater than the preset acquisition frequency can be used to obtain the inner wall image of the culvert, so as to acquire images of the inner wall of the culvert at a shorter time interval.

[0037] After collecting the inner wall image of the culvert, the inner wall image of the culvert can be input into the first detection model for image detection to obtain the detection result of the culvert, so that the inner wall image of the culvert can be detected more frequently and faster when the rainfall information meets the preset rainfall information, thereby improving the timeliness and detection efficiency of the culvert inner wall detection.

[0038] Considering that the first detection model is a lightweight model, the reliability of its detection result may be poor. Therefore, in order to improve the reliability of the detection result of the culvert obtained by the first detection model, in some embodiments, each of the inner wall images is input into the first detection model for image detection to obtain the detection result of the culvert, including: Inputting any target image among the inner wall images into the first detection model for image detection to obtain a detection result of the target image; Determine that the detection result of the target image is that there is no inner wall defect, and the detection result obtained by inputting the inner wall image of the previous frame of the target image into the first detection model is that there is no inner wall defect, and determine that the detection result of the culvert at the target moment of obtaining the target image is that there is no inner wall defect.

[0039] In some embodiments, the target image is any frame of the inner wall image. Each frame of the inner wall image has a corresponding timestamp, which is the target time when the inner wall image is acquired. If the time when a certain inner wall image is acquired is T1, then the target time corresponding to the inner wall image is T1.

[0040] When the first detection model is used for culvert detection, the target image can be input into the first detection model to obtain the detection result of the target image. If the detection result of the target image is that there is no inner wall defect, that is, no image area with inner wall defects is detected from the target image, it means that at this time, through the first detection model, it is detected that there is no inner wall defect in the culvert at the target time corresponding to the target image. Since each inner wall image is taken in chronological order, if there is no inner wall defect in the target image, then the previous frame of the inner wall image of the target image must also not have an inner wall defect. At this time, the detection result obtained by inputting the previous frame of the inner wall image of the target image into the first detection model can be obtained, and the detection result can be compared with the detection result obtained by inputting the target image into the first detection model to verify the reliability of the detection result that there is no inner wall defect in the target image. If the detection result obtained by inputting the previous frame of the inner wall image into the first detection model is that there is an inner wall defect, it can be determined that the detection result that there is no inner wall defect in the target image is unreliable, and the detection result of the target image is ignored at this time.

[0041] If the detection result obtained by inputting the previous frame of inner wall image into the first detection model is also that there is no inner wall defect, then it can be determined that the detection result that there is no inner wall defect in the target image is reliable. At this time, it can be determined that the detection result of the culvert at the target moment of acquiring the target image is that there is no inner wall defect.

[0042] In this way, the reliability of the detection result that there is no inner wall defect in the target image can be verified by inputting the detection result obtained by the first detection model through the previous frame of the inner wall image, so as to improve the reliability of the detection result of the culvert obtained by the first detection model.

[0043] Alternatively, in some embodiments, each of the inner wall images is input into a first detection model for image detection to obtain a detection result of the culvert, including: Inputting any target image among the inner wall images into the first detection model for image detection to obtain a detection result of the target image; Determine that the detection result of the target image is that there is an inner wall defect, and the detection result obtained by inputting the inner wall image of the next frame of the target image into the first detection model is that there is an inner wall defect, and determine that the detection result of the culvert at the target moment of obtaining the target image is that there is an inner wall defect.

[0044] In some embodiments, if the detection result of the target image is that there is an inner wall defect, that is, the image area of ​​the inner wall defect is detected from the target image, it means that at this time, through the first detection model, it is detected that the culvert has an inner wall defect at the target time corresponding to the target image. Since each inner wall image is taken in chronological order, if there is an inner wall defect in the target image, the next frame of the inner wall image of the target image must also have an inner wall defect. At this time, the detection result obtained by inputting the next frame of the inner wall image of the target image into the first detection model can be obtained, and the detection result can be compared with the detection result obtained by inputting the target image into the first detection model to verify the reliability of the detection result that the target image has an inner wall defect. If the detection result obtained by inputting the next frame of the inner wall image into the first detection model is that there is no inner wall defect, it can be determined that the detection result that the target image has an inner wall defect is unreliable, and the detection result of the target image is ignored at this time.

[0045] If the next frame of inner wall image input into the first detection model also obtains the detection result of inner wall defects, it can be determined that the detection result of the target image having inner wall defects is reliable. At this time, it can be determined that the detection result of the culvert at the target moment of acquiring the target image is that there are inner wall defects.

[0046] In this way, the reliability of the detection result that the target image has inner wall defects can be verified by inputting the next frame of inner wall image into the first detection model to obtain the detection result, so as to improve the reliability of the culvert detection result obtained by the first detection model.

[0047] In some embodiments, according to the matching result between the rainfall information and the preset rainfall information, at least one frame of the inner wall image of the culvert acquired within a preset time interval is input into the first detection model or the second detection model for image detection to obtain the detection result of the culvert, including: Determining that the rainfall information does not meet the preset rainfall information, acquiring the inner wall images of the culvert according to the preset acquisition frequency within the preset time interval; Each of the inner wall images is input into a second detection model for image detection to obtain a detection result of the culvert.

[0048] In some embodiments, if the rainfall information in the area where the culvert is located does not meet the preset rainfall information, such as the rainfall is less than the preset rainfall, even if there is an inner wall defect that causes leakage in the inner wall of the culvert, it is highly unlikely to cause leakage in the culvert. At the same time, since there is no leakage in the culvert, the change in the image area corresponding to the inner wall defect is also relatively subtle. Based on this, the inner wall image of the culvert can be acquired within a preset time interval at a frequency greater than the preset acquisition frequency, so as to acquire images of the inner wall of the culvert at a longer time interval.

[0049] After collecting the inner wall image of the culvert, the inner wall image of the culvert can be input into the second detection model for image detection to obtain the detection result of the culvert, thereby reducing the invalid detection of the inner wall image of the culvert when the rainfall information does not meet the preset rainfall information. At the same time, the inner wall image can be more accurately detected through the second detection model to reduce the resource occupation of the detection of the inner wall of the culvert while improving the accuracy of the detection of the inner wall of the culvert.

[0050] In order to more accurately determine whether the rainfall information in the area where the culvert is located will cause leakage in the culvert, in some embodiments, the method further includes: When the rainfall amount in the rainfall information reaches the preset rainfall amount in the preset rainfall information, or the rainfall speed in the rainfall information reaches the preset rainfall speed in the preset rainfall information, it is determined that the rainfall information meets the preset rainfall information.

[0051] In some embodiments, the rainfall information includes rainfall amount and rainfall speed, and the preset rainfall information includes preset rainfall amount and preset rainfall speed. If the rainfall amount reaches the preset rainfall amount, or the rainfall speed reaches the preset rainfall speed, it means that the defects of the inner wall of the culvert will cause leakage in the culvert, and it is easier to find the inner wall defects that cause leakage. At this time, it can be determined that the rainfall information meets the preset rainfall information, so that the image detection of the inner wall image of the culvert can be performed through the first detection model.

[0052] In some embodiments, if the rainfall is less than the preset rainfall, and the rainfall speed is less than the preset rainfall speed, it means that even if there are inner wall defects such as cracks on the inner wall of the culvert, it is highly unlikely that the culvert will leak, and it is difficult to find the inner wall defects that cause the leakage. At this time, it can be determined that the rainfall information does not meet the preset rainfall information, so that image detection of the inner wall image of the culvert can be performed through the second detection model.

[0053] The culvert detection device provided in the present application is described below. The culvert detection device described below and the culvert detection method described above can refer to each other.

[0054] In one embodiment, if Figure 2 As shown, a culvert detection device is provided, comprising: The information acquisition module 210 is used to obtain rainfall information in the area where the culvert is located; The culvert detection module 220 is used to input at least one frame of the inner wall image of the culvert acquired within a preset time interval into the first detection model or the second detection model for image detection according to the matching result of the rainfall information and the preset rainfall information, so as to obtain the detection result of the culvert; The preset time interval is the time interval between the moment of obtaining the rainfall information this time and the moment of obtaining the rainfall information next time; The matching result is that the rainfall information meets the preset rainfall information, and the inner wall image is input into the first detection model for image detection; or the matching result is that the rainfall information does not meet the preset rainfall information, and the inner wall image is input into the second detection model for image detection, and the first detection model is smaller than the second detection model.

[0055] By matching the rainfall information of the area where the culvert is located with the preset rainfall information, when the rainfall information meets the preset rainfall information, the inner wall image is input into the first detection model for image detection, or when the rainfall information does not meet the preset rainfall information, the inner wall image is input into the second detection model that is larger than the first detection model for image detection, so as to obtain the detection result of the culvert, thereby minimizing the accuracy of the detection of the inner wall of the culvert, reducing the resource occupation of the detection of the inner wall of the culvert, and improving the detection efficiency of the culvert.

[0056] In one embodiment, the culvert detection module 220 is specifically used for: Determining that the rainfall information satisfies preset rainfall information, and acquiring the inner wall images of the culvert according to a target acquisition frequency greater than a preset acquisition frequency within the preset time interval; Each of the inner wall images is input into a first detection model for image detection to obtain a detection result of the culvert.

[0057] In one embodiment, the culvert detection module 220 is specifically used for: Inputting any target image among the inner wall images into the first detection model for image detection to obtain a detection result of the target image; Determine that the detection result of the target image is that there is no inner wall defect, and the detection result obtained by inputting the inner wall image of the previous frame of the target image into the first detection model is that there is no inner wall defect, and determine that the detection result of the culvert at the target moment of obtaining the target image is that there is no inner wall defect.

[0058] In one embodiment, the culvert detection module 220 is specifically used for: Inputting any target image among the inner wall images into the first detection model for image detection to obtain a detection result of the target image; Determine that the detection result of the target image is that there is an inner wall defect, and the detection result obtained by inputting the inner wall image of the next frame of the target image into the first detection model is that there is an inner wall defect, and determine that the detection result of the culvert at the target moment of obtaining the target image is that there is an inner wall defect.

[0059] In one embodiment, the culvert detection module 220 is specifically used for: Determining that the rainfall information does not meet the preset rainfall information, acquiring the inner wall images of the culvert according to the preset acquisition frequency within the preset time interval; Each of the inner wall images is input into a second detection model for image detection to obtain a detection result of the culvert.

[0060] In one embodiment, the culvert detection module 220 is further configured to: When the rainfall amount in the rainfall information reaches the preset rainfall amount in the preset rainfall information, or the rainfall speed in the rainfall information reaches the preset rainfall speed in the preset rainfall information, it is determined that the rainfall information meets the preset rainfall information.

[0061] In one embodiment, the culvert detection module 220 is further configured to: If the rainfall amount in the rainfall information is less than the preset rainfall amount in the preset rainfall information, and the rainfall speed in the rainfall information is less than the preset rainfall speed in the preset rainfall information, it is determined that the rainfall information does not meet the preset rainfall information.

[0062] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call a computer program in the memory 830 to execute a culvert detection method, for example, including: Get rainfall information in the area where the culvert is located; According to the matching result between the rainfall information and the preset rainfall information, at least one frame of the inner wall image of the culvert acquired within the preset time interval is input into the first detection model or the second detection model for image detection to obtain the detection result of the culvert; The preset time interval is the time interval between the moment of obtaining the rainfall information this time and the moment of obtaining the rainfall information next time; The matching result is that the rainfall information meets the preset rainfall information, and the inner wall image is input into the first detection model for image detection; or the matching result is that the rainfall information does not meet the preset rainfall information, and the inner wall image is input into the second detection model for image detection, and the first detection model is smaller than the second detection model.

[0063] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0064] On the other hand, an embodiment of the present application further provides a storage medium, the storage medium includes a computer program, the computer program can be stored on a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the culvert detection method provided in the above embodiments, for example, including: Get rainfall information in the area where the culvert is located; According to the matching result between the rainfall information and the preset rainfall information, at least one frame of the inner wall image of the culvert acquired within the preset time interval is input into the first detection model or the second detection model for image detection to obtain the detection result of the culvert; The preset time interval is the time interval between the moment of obtaining the rainfall information this time and the moment of obtaining the rainfall information next time; The matching result is that the rainfall information meets the preset rainfall information, and the inner wall image is input into the first detection model for image detection; or the matching result is that the rainfall information does not meet the preset rainfall information, and the inner wall image is input into the second detection model for image detection, and the first detection model is smaller than the second detection model.

[0065] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

[0066] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A culvert detection method, characterized in that: include: Get rainfall information in the area where the culvert is located; According to the matching result between the rainfall information and the preset rainfall information, at least one frame of the inner wall image of the culvert acquired within the preset time interval is input into the first detection model or the second detection model for image detection to obtain the detection result of the culvert; The preset time interval is the time interval between the moment of obtaining the rainfall information this time and the moment of obtaining the rainfall information next time; The matching result is that the rainfall information meets the preset rainfall information, and the inner wall image is input into the first detection model for image detection; or the matching result is that the rainfall information does not meet the preset rainfall information, and the inner wall image is input into the second detection model for image detection, and the first detection model is smaller than the second detection model.

2. The culvert detection method according to claim 1, characterized in that: According to the matching result between the rainfall information and the preset rainfall information, at least one frame of the inner wall image of the culvert acquired within the preset time interval is input into the first detection model or the second detection model for image detection to obtain the detection result of the culvert, including: Determining that the rainfall information satisfies preset rainfall information, and acquiring the inner wall images of the culvert according to a target acquisition frequency greater than a preset acquisition frequency within the preset time interval; Each of the inner wall images is input into a first detection model for image detection to obtain a detection result of the culvert.

3. The culvert detection method according to claim 2, characterized in that: Inputting each of the inner wall images into a first detection model for image detection to obtain a detection result of the culvert, including: Inputting any target image among the inner wall images into the first detection model for image detection to obtain a detection result of the target image; Determine that the detection result of the target image is that there is no inner wall defect, and the detection result obtained by inputting the inner wall image of the previous frame of the target image into the first detection model is that there is no inner wall defect, and determine that the detection result of the culvert at the target moment of obtaining the target image is that there is no inner wall defect.

4. The culvert detection method according to claim 2, characterized in that: Inputting each of the inner wall images into a first detection model for image detection to obtain a detection result of the culvert, including: Inputting any target image among the inner wall images into the first detection model for image detection to obtain a detection result of the target image; Determine that the detection result of the target image is that there is an inner wall defect, and the detection result obtained by inputting the inner wall image of the next frame of the target image into the first detection model is that there is an inner wall defect, and determine that the detection result of the culvert at the target moment of obtaining the target image is that there is an inner wall defect.

5. The culvert detection method according to claim 1, characterized in that: According to the matching result between the rainfall information and the preset rainfall information, at least one frame of the inner wall image of the culvert acquired within the preset time interval is input into the first detection model or the second detection model for image detection to obtain the detection result of the culvert, including: Determining that the rainfall information does not meet the preset rainfall information, acquiring the inner wall images of the culvert according to the preset acquisition frequency within the preset time interval; Each of the inner wall images is input into a second detection model for image detection to obtain a detection result of the culvert.

6. The culvert detection method according to any one of claims 1 to 5, characterized in that: Also includes: When the rainfall amount in the rainfall information reaches the preset rainfall amount in the preset rainfall information, or the rainfall speed in the rainfall information reaches the preset rainfall speed in the preset rainfall information, it is determined that the rainfall information meets the preset rainfall information.

7. The culvert detection method according to any one of claims 1 to 5, characterized in that: Also includes: If the rainfall amount in the rainfall information is less than the preset rainfall amount in the preset rainfall information, and the rainfall speed in the rainfall information is less than the preset rainfall speed in the preset rainfall information, it is determined that the rainfall information does not meet the preset rainfall information.

8. A culvert detection device, characterized in that: include: An information acquisition module is used to obtain rainfall information in the area where the culvert is located; A culvert detection module, for inputting at least one frame of inner wall image of the culvert acquired within a preset time interval into a first detection model or a second detection model for image detection according to a matching result between the rainfall information and preset rainfall information, so as to obtain a detection result of the culvert; The preset time interval is the time interval between the moment of obtaining the rainfall information this time and the moment of obtaining the rainfall information next time; The matching result is that the rainfall information meets the preset rainfall information, and the inner wall image is input into the first detection model for image detection; or the matching result is that the rainfall information does not meet the preset rainfall information, and the inner wall image is input into the second detection model for image detection, and the first detection model is smaller than the second detection model.

9. An electronic device comprising a processor and a memory storing a computer program, characterized in that: When the processor executes the computer program, the culvert detection method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the culvert detection method according to any one of claims 1 to 7 is implemented.

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