Bridge inspection method, bridge inspection device, and program

Abstract

The extent of damage to bridges is determined automatically and with high accuracy. [Solution] The bridge inspection device 110 inputs a prompt containing damage criterion information explaining the criteria for the degree of damage to a bridge, and image data to be inspected, into the language model 121, and obtains response information from the language model 121 that contains damage degree information indicating the degree of damage corresponding to the image to be inspected.

Description

Technical Field

[0006] , , , , , , ,

[0005]

[0001] The present disclosure relates to a bridge inspection method, a bridge inspection device, and a program.

Background Art

[0002] As a method for inspecting the degree of damage to a bridge, Patent Document 1 discloses that an inspector determines the degree of deterioration of the lower surface and the side surface of a partial beam of a trestle, respectively. Further, Patent Document 1 discloses that an inspector observes a target partial beam in a beam structure and determines the soundness of the partial beam in comparison with a predetermined determination criterion.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Non-Patent Documents

[0007] The bridge inspection method of this disclosure is characterized by comprising: a prompt input step of inputting a prompt containing damage criterion information describing the criteria for the degree of damage to a bridge into a language model; an inspection target image input step of inputting image data corresponding to the damage into the language model; and a response acquisition step of obtaining response information from the language model containing damage degree information indicating the degree of damage corresponding to the image data.

[0008] The bridge inspection device of this disclosure is characterized by comprising: a prompt input unit that inputs a prompt containing damage criterion information explaining the criteria for the degree of damage to a bridge to a language model; an inspection target image input unit that inputs image data corresponding to the damage to the language model; and a response acquisition unit that acquires response information from the language model containing damage degree information indicating the degree of damage corresponding to the image data.

[0009] The program of this disclosure causes a computer to perform the following steps: a prompt input step of inputting a prompt containing damage criterion information describing the criteria for the degree of damage to a bridge into a language model; an inspection target image input step of inputting image data corresponding to the damage into the language model; and a response acquisition step of obtaining response information from the language model containing damage degree information indicating the degree of damage corresponding to the image data. [Effects of the Invention]

[0010] According to this disclosure, the extent of damage to a bridge can be determined automatically and with high accuracy. [Brief explanation of the drawing]

[0011] [Figure 1] This is a diagram showing an example of the configuration of a bridge inspection system. [Figure 2]It is a diagram showing an example of the functional configuration of a bridge inspection device. [Figure 3] It is a diagram explaining an example of the type of damage. [Figure 4A] It is a diagram showing an example of an inspection screen before inspection starts. [Figure 4B] It is a diagram showing an example of an inspection screen after inspection. [Figure 5] It is a diagram showing an example of an inspection target image. [Figure 6] It is a diagram showing an example of an inspection target image with tag information attached. [Figure 7] It is a diagram showing an example of information associated with a prompt. [Figure 8A] It is a diagram showing an example of a set of prompts. [Figure 8B] It is a diagram showing an example of a set of prompts following Figure 8A. [Figure 8C] It is a diagram showing an example of a set of prompts following Figure 8B. [Figure 8D] It is a diagram showing an example of a set of prompts following Figure 8C. [Figure 8E] It is a diagram showing an example of a set of prompts following Figure 8D. [Figure 8F] It is a diagram showing an example of a set of prompts following Figure 8E. [Figure 8G] It is a diagram showing an example of a set of prompts following Figure 8F. [Figure 8H] It is a diagram showing an example of a set of prompts following Figure 8G. [Figure 9] It is a diagram showing an example of metadata. [Figure 10] It is a diagram showing an example of response information. [Figure 11] It is a flowchart explaining an example of a bridge inspection method. [Figure 12A] It is a diagram showing a first specific example of a prompt. [Figure 12B] It is a diagram showing a first specific example of a prompt following Figure 12A. [Figure 12C] It is a diagram showing a first specific example of a prompt following Figure 12B. [Figure 13A] This figure shows a second concrete example of a prompt. [Figure 13B] This figure shows a second specific example of the prompt that follows Figure 13A. [Figure 14A] This figure shows a third specific example of a prompt. [Figure 14B] This figure shows a third specific example of the prompt following Figure 14A. [Figure 14C] This figure shows a third specific example of the prompt that follows Figure 14B. [Figure 15] This figure shows a fourth concrete example of a prompt. [Figure 16A] This figure shows a fifth specific example of a prompt. [Figure 16B] This figure shows a fifth specific example of the prompt following Figure 16A. [Figure 17A] This figure shows a sixth specific example of a prompt. [Figure 17B] This figure shows a sixth specific example of the prompt that follows Figure 17A. [Modes for carrying out the invention]

[0012] Hereinafter, an embodiment of this disclosure will be described with reference to the drawings. Figure 1 shows an example of the configuration of the bridge inspection system according to this embodiment. Figure 1 illustrates a case where the bridge inspection system comprises a bridge inspection device 110 and a language model server 120. Figure 1 also illustrates a case where the bridge inspection device 110 and the language model server 120 are connected to each other via a network 130, including the Internet. The language model server 120 may reside on the cloud. The language model server 120, in response to requests from external devices such as the bridge inspection device 110, causes a language model 121, trained by machine learning using a large amount of data, to perform natural language processing and transmits the results to the external device. By utilizing the language model 121, for example, machine translation, text generation, and answering questions can be performed automatically. The language model 121 used in this embodiment is a trained model, such as a large language model (LLM) like GPT4-o(registered trademark), LaMDA(registered trademark), LLaMA(registered trademark), and LLaVA(registered trademark).

[0013] The bridge inspection device 110 is a device that processes information to determine (inspect) the degree of damage in the part of a bridge to be inspected. The part of the bridge to be inspected (the part that is inspected) may be the entirety of one or more members of the bridge, a part of one or more members of the bridge, or the entire bridge. In the following description, the part of the bridge to be inspected will be abbreviated as the inspection target part as needed.

[0014] In this embodiment, we illustrate a case in which the language model 121 outputs a result of determining the degree of damage to the bridge as response information based on the information input from the bridge inspection device 110. Furthermore, in this embodiment, we illustrate a case in which the information input from the bridge inspection device 110 includes prompts and images of the part to be inspected. For this reason, the language model used in this embodiment is preferably a language model that can recognize images in addition to characters. Examples of such language models include GPT4-o(registered trademark) and LLaVA 1.5(registered trademark).

[0015] The bridge inspection device 110 includes, for example, one or more hardware processors such as a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit), and one or more memory modules such as RAM (Random Access Memory) and ROM (Read Only Memory). It performs various calculations by executing one or more programs stored in memory using one or more hardware processors. The bridge inspection device 110 also includes, for example, a NIC (Network Interface Card) for connecting the bridge inspection device 110 to a network.

[0016] Furthermore, a storage medium that can be read by the bridge inspection device 110 (computer) may be connected to the bridge inspection device 110. Note that this storage medium is not limited to a storage medium that can only read data; it may also be a storage medium that can be read and written by the bridge inspection device 110 (computer). In the following description, a computer-readable storage medium, including a computer-readable storage medium, will be abbreviated as "storage medium" as needed. Additionally, an output device such as a computer display may be connected to the bridge inspection device 110. Furthermore, an input device such as a keyboard or mouse may be connected to the bridge inspection device 110. Information input to the bridge inspection device 110 may also be performed using a GUI (Graphical User Interface).

[0017] Furthermore, the bridge inspection device 110 may be implemented using dedicated hardware such as an ASIC (Application Specific Integrated Circuit) and an FPGA (Field Programmable Gate Array). The language model server 120 can also be implemented, for example, by using the same hardware as the bridge inspection device 110. Depending on the performance of the bridge inspection device 110, the language model 121 may be stored in the bridge inspection device 110.

[0018] Figure 2 shows an example of the functional configuration of the bridge inspection device 110. In this embodiment, Figure 2 illustrates a case where the bridge inspection device 110 comprises a prompt acquisition unit 111, an inspection target image acquisition unit 112, a metadata acquisition unit 113, a reference image acquisition unit 114, a tag information assignment unit 115, an operation reception unit 116, an input unit 117, an answer acquisition unit 118, and a display control unit 119.

[0019] As described above, the bridge inspection device 110 of this embodiment performs information processing to determine (inspect) the degree of damage in the part to be inspected. In this embodiment, we will illustrate the case where the types of damage in the part to be inspected are the 26 types described in "Table 5.1.2 Standard Method for Understanding the Condition" of Non-Patent Literature 1. Figure 3 shows "Table 5.1.2" of Non-Patent Literature 1. In this embodiment, we will illustrate the case in which the degree of these 26 types of damage is automatically determined (inspected) by the bridge inspection device 110. An example of the functions of each part of the bridge inspection device 110 will be described below.

[0020] <Operation reception unit 116, display control unit 119> The operation reception unit 116 receives input operations from the operator for the bridge inspection device 110. These input operations are performed, for example, by using an input device such as a keyboard or mouse. The display control unit 119 displays, for example, information necessary for inspecting the extent of damage to the bridge and information indicating the results of the inspection on the extent of damage to the bridge on the display unit 140. The display unit 140 is implemented, for example, by using an output device such as the computer display described above. In this embodiment, an example is given in which the information necessary for inspecting the extent of damage to the bridge and information indicating the results of the inspection on the extent of damage to the bridge are displayed on the inspection screen.

[0021] Figure 4A shows an example of the inspection screen 410 before inspection. In this embodiment, the case where the inspection screen 410 before inspection has GUI functionality is illustrated. For example, the operator performs an input operation to the input device to call up the pre-inspection screen 410 in order to start the inspection of the part to be inspected on the bridge inspection device 110. The display control unit 119 displays the pre-inspection screen 410 on the display unit 140 in response to the operation reception unit 116 receiving the input operation.

[0022] Figure 4A illustrates a case where the inspection screen 410 before inspection displays the damage type specification field 411, the additional information specification field 412, the image to be inspected specification field 413, the image file to be inspected display field 414, the image to be inspected display field 415, and the inspection execution command button 416.

[0023] In the damage type specification field 411, the type of damage to be inspected is specified. The type of damage specified in the damage type specification field 411 is determined based on the operator's input. Figure 4A illustrates a case where the type of damage is selected by a pull-down menu in the damage type specification field 411, but the type of damage may also be specified by other GUIs. In this embodiment, as will be described later, prompts are prepared in advance for each type of damage, and an example is given where the prompt corresponding to the type of damage specified in the damage type specification field 411 is acquired by the prompt acquisition unit 111, which will be described later. In this embodiment, if the prompt acquired by the prompt acquisition unit 111 includes referring to reference image data, an example is given where the reference image data is acquired by the reference image acquisition unit 114. The reference image data is data of a reference image that the language model 121 refers to when determining the degree of damage to the part to be inspected. The reference image is, for example, a photograph of a part to be inspected of the same type as the part to be inspected, and is a photograph of the part to be inspected in an actually damaged state. In this embodiment, an example is given where images corresponding to each of the multiple stages of damage are used as such reference images. For example, if the degree of damage is expressed in five stages, reference image data corresponding to each of the five stages of damage is prepared in advance.

[0024] Additional information is specified in the additional information specification field 412. The specification of additional information in the additional information specification field 412 is performed based on the operator's input. Figure 4A illustrates the case where additional information is selected by a pull-down menu in the additional information specification field 412, but additional information may also be specified using other GUIs.

[0025] Additional information may include, for example, key information for selecting a prompt. This additional information may include, for example, information indicating at least one of the following: the material of the part to be inspected, the painting specifications of the part to be inspected, and the bridge members. However, the additional information may also include other information in place of these (i.e., the additional information may not include information indicating the material of the part to be inspected, the painting specifications of the part to be inspected, and the bridge members). Material information is represented by information determined based on the composition of the materials that make up the bridge members, for example. For example, the material may be represented by a name or code that indicates the type of material. Painting specifications are the specifications for painting on the surface of the bridge (the part to be inspected). Painting specifications include, for example, whether or not painting is done and, if painted, the paint color. The paint color may be one color or multiple colors. Also, for example, the area ratio of multiple colors on the surface of the bridge members may be included in the painting specifications.

[0026] In Figure 4A, an example is shown where only concrete, an example of the material of the part to be inspected, is selected as additional information. Multiple pieces of information (for example, information combining material and paint specifications) may be included in the pull-down menu as additional information. Furthermore, as will be described later, in this embodiment, prompts are prepared in advance for each piece of additional information, and an example is shown where the prompt corresponding to the additional information specified in the damage type specification field 411 is acquired by the prompt acquisition unit 111, which will be described later. For example, if the material of the part to be inspected, the paint specifications of the part to be inspected, and the bridge members are included in the additional information, prompts are prepared in advance for each material of the part to be inspected, each paint specification, and each member. Then, the prompts corresponding to the material of the part to be inspected, the paint specifications of the part to be inspected, and the bridge members specified in the additional information specification field 412 are acquired by the prompt acquisition unit 111, which will be described later. Note that if, for example, only one or two of the material of the part to be inspected, the paint specifications of the part to be inspected, and the bridge members are specified in the additional information specification field 412, the prompts are selected as if the remaining items are not specified.

[0027] In the Inspection Target Image Specification Field 413, the data file of the image of the part to be inspected is specified. The specification of the data file of the image of the part to be inspected in the Inspection Target Image Specification Field 413 is performed based on the operator's input. In the following description, the image of the part to be inspected will be referred to as the Inspection Target Image as needed, and the data of the Inspection Target Image will be referred to as the Inspection Target Image Data as needed. Figure 4A illustrates a case in which the Inspection Target Image Data File is specified by the operator dragging and dropping the Inspection Target Image Data File into the Inspection Target Image Specification Field 413. Figure 4A also illustrates a case in which the Inspection Target Image Data File is specified by the operator pressing the File Selection Button 413a. The Inspection Target Image Data File may also be specified by other GUI methods. In this embodiment, an example is given in which the Inspection Target Image Data specified in this way in the Inspection Target Image Specification Field 413 is acquired by the Inspection Target Image Acquisition Unit 112, which will be described later.

[0028] The inspection target image file display area 414 displays summary information (file name, file format, etc.) of the inspection target image data specified in the inspection target image specification area 413. Figure 4A illustrates a case where, when the delete button 414a displayed in the inspection target image file display area 414 is pressed by an operator, the operation reception unit 116 cancels the specification of the inspection target image data file displayed in the inspection target image file display area 414, and the display control unit 119 clears the display of the summary information of the said inspection target image data file.

[0029] The inspection target image display area 415 displays the inspection target image indicated by the inspection target image data specified in the inspection target image specification area 413. The inspection execution instruction button 416 is a button pressed by an operator to initiate the inspection of the part to be inspected by the bridge inspection device 110. In this embodiment, when the inspection execution instruction button 416 is pressed, metadata is acquired by the metadata acquisition unit 113 (described later), and the prompt acquired by the prompt acquisition unit 111, the inspection target image data acquired by the inspection target image acquisition unit 112, and the metadata acquired by the metadata acquisition unit 113 are input to the language model 121 from the input unit 117 (described later). As will be described in detail later, the metadata is data that indicates the specifications of the response information.

[0030] The display control unit 119 displays information based on the response information from the language model 121 on the display unit 140. An example of how the response information is displayed will be described later in the section on <Display Control Unit 119>.

[0031] <Image acquisition unit 112 for the subject of inspection> The inspection target image acquisition unit 112 acquires data of the image captured from the inspection target (inspection target image data). From the viewpoint of performing inspections with higher accuracy, it is preferable that the inspection target image data be a color image, but a grayscale image or the like may also be used. In this embodiment, the case in which the inspection target image data is a still image is given as an example.

[0032] The inspection target image acquisition unit 112 acquires the inspection target image data by, for example, reading the file specified in the inspection target image specification field 413 from among the inspection target image data files stored on the storage medium. The storage medium may be located inside or outside the bridge inspection device 110.

[0033] Figure 5 shows an example of an image 500 to be inspected. Figure 5 illustrates a case where the image 500 to be inspected includes an area 510 where corrosion has occurred. In addition, Figure 5 illustrates a case where handwritten linear patterns 511a to 511d and handwritten characters 512a to 512c are marked on the area to be inspected with chalk or the like.

[0034] <Tag information assignment unit 115> The tag information assignment unit 115 assigns first to Nth tag information to the image 500 to be inspected. Here, N is an integer of 2 or more. The first to Nth tag information is used, for example, to indicate which region the language model 121 evaluated as having a degree of damage in the response information. In this embodiment, we illustrate a case where the tag information assignment unit 115 embeds the identification numbers of the first to Nth regions at representative positions of the first to Nth regions of the image 500 to be inspected. Figure 6 shows an example of an image 500 to be inspected to which tag information has been assigned. In the following description, an image to be inspected to which tag information has been assigned will be referred to as a tagged image to be inspected as needed.

[0035] Figure 6 illustrates a case where the tag information assignment unit 115 divides the image to be inspected 500 into 48 equal regions and embeds numbers from 0 to 47 at representative positions in these 48 regions. In this case, the aforementioned N is 48. For example, the tag information assignment unit 115 may divide the image to be inspected 500 into N regions (N=48 in Figure 6) in a grid pattern and embed tag information (numbers) for each region within the grid at the corresponding positions. In this case, for example, if the value of N is set in advance, the tag information assignment unit 115 can mechanically (automatically) assign the first to the Nth tag information corresponding to the first to the Nth regions of the image to be inspected 500. In this embodiment, an example is given in which the image data to be inspected includes first tag information corresponding to the first region included in the image shown by the image data to be inspected, and second tag information corresponding to the second region included in the image data to be inspected.

[0036] Here, it is preferable that the tag information assignment unit 115 assigns first to nth tag information in a form that the language model 121 can reliably distinguish from the information originally displayed on the image 500 to be inspected. This is because it can suppress the language model 121 from mistakenly identifying the first to nth tag information as the information originally displayed on the image 500 to be inspected. For example, as mentioned above, the image 500 to be inspected may contain handwritten numbers. In this case, if the form of the numbers assigned as first to nth tag information is similar to the form of the numbers originally displayed on the image 500 to be inspected, the language model 121 may mistakenly recognize the first to nth tag information as the information to be inspected. Therefore, it is preferable that the tag information assignment unit 115 assigns numbers to the image 500 to be inspected as first to nth tag information that have a different form from the numbers originally displayed on the image 500 to be inspected. For example, the tag information assignment unit 115 may assign to the inspection target image 500, as first to Nth tag information, numbers that differ from the numbers originally displayed on the inspection target image 500 in at least one of their color and font. The format of the first to Nth tag information may be pre-set for the bridge inspection device 110, for example.

[0037] It should be noted that the tag information assignment unit 115 is not necessarily required to assign the first to Nth tag information to the inspection image 500 in the manner described above. For example, the tag information assignment unit 115 may assign the first to Nth tag information to the inspection image 500 by performing segmentation (object identification) and numbering of the inspection image 500 using an artificial intelligence model capable of image segmentation. For example, a SAM (Segment Anything Model) may be used as such an artificial intelligence model. When using a SAM, for example, by inputting a prompt containing information describing the object (the part to be inspected for the degree of damage) into the SAM, the object (the part to be inspected for the degree of damage) is extracted from the inspection image 500, and numbering (assignment of the first to Nth tag information) is performed on the region of the extracted object.

[0038] <Prompt acquisition unit 111> The prompt acquisition unit 111 acquires prompts to be input to the language model 121 when causing the language model 121 to determine the degree of damage in the inspection target unit (inspection target image 500).

[0039] In this embodiment, as illustrated in Figure 7, a case is shown in which prompts for each type of damage and each piece of additional information are stored in a storage medium. In Figure 7, the prompt column stores prompt identification information, etc. The prompt acquisition unit 111 acquires the prompts corresponding to the type of damage specified in the damage type specification column 411 and the additional information specified in the additional information specification column 412 from among the prompts stored in the storage medium, for example, by reading the prompts corresponding to the type of damage and additional information specified by the operator. The storage medium may be located inside or outside the bridge inspection device 110.

[0040] Each prompt contains damage criteria information that describes the criteria for the degree of damage to the bridge. To improve the accuracy of determining the degree of damage to the bridge, each prompt may also contain at least one of the following: damage type information, material information, damage definition information, and damage criteria information.

[0041] Damage type information is information that describes the type of damage. Note that the damage type information may describe the type of damage by its name (i.e., only the name of the damage type may be included in the damage type information). Furthermore, the damage type information may also include text that describes the type of damage.

[0042] Material information is information that describes the material of the part being inspected. Note that the material may be described by its name (i.e., only the name of the material may be included in the material information). Furthermore, the material information may also include text describing the characteristics of the material.

[0043] Damage definition information is information that describes the definition of the type of damage. Defined damages include, for example, at least one of the following: corrosion, water leakage / free lime, spalling / rebar exposure, and delamination. In addition, other types of damage may be defined, for example, the types of damage illustrated in Figure 3.

[0044] Damage criteria information is information that explains the criteria for the degree of damage to a bridge. When inspecting the degree of corrosion as the degree of damage to a bridge, the damage criteria information may include at least one of the following: damage extent information and damage depth information. Damage extent information is information that explains the criteria for the extent of the damage. Damage depth information is information that explains the criteria for the depth of the damage.

[0045] Figure 7 illustrates a case where prompts are pre-prepared for each type of damage and for each piece of additional information. However, this is not always necessary. For example, the prompts do not have to be for each piece of additional information. The prompts may also be for each piece of information other than the type of damage and the additional information. Furthermore, the prompts may correspond to each of the first to nth tag information described above. In this case, for example, prompts corresponding to the additional information in the first to nth regions corresponding to the first to nth tag information (material of the part to be inspected, painting specifications of the part to be inspected, and bridge members) may be selected for each of the first to nth regions. In this case, the bridge inspection device 110 may include a feature information calculation unit that calculates the feature information of the part to be inspected contained in the inspection target image 500 using an artificial intelligence model. The feature information of the part to be inspected includes additional information (for example, at least one of the material of the part to be inspected, painting specifications of the part to be inspected, and bridge members). For example, a CNN (Convolutional Neural Network) may be used as such an artificial intelligence model.

[0046] Figures 8A to 8H show an example of a prompt set 800. A prompt set is a collection of multiple prompts for the target to be acquired (selected) by the prompt acquisition unit 111. In this embodiment, one prompt is defined as a set of prompts that the language model 121 inputs to determine the degree of damage to the part under inspection once. Also, for the sake of notation, one prompt set 800 is shown separately in Figures 8A to 8H.

[0047] Figures 8A to 8H illustrate the case where the prompt set 800 contains four prompts. The first prompt includes damage type information 810a, material information 820a, damage definition information 830a, damage criteria information 840a, and judgment example information 850a. The second prompt includes damage type information 810b, material information 820b, damage definition information 830b, damage criteria information 840b, and judgment example information 850b. The third prompt includes damage type information 810c, material information 820c, damage definition information 830c, and damage criteria information 840c. The fourth prompt includes damage type information 810d, material information 820d, damage definition information 830d, damage criteria information 840d, and judgment example information 850d.

[0048] The example judgment information is, for example, information that shows examples of how to determine the degree of damage using reference image data. The example judgment information includes, for example, information about the storage location of the reference image data and information that describes the degree of damage to the reference image data.

[0049] The information shown after (Area) and (Depth) in the damage criteria information 840a to 840d is an example of the damage area information and damage depth information mentioned above, respectively. Note that the third prompt does not contain any example judgment information. Thus, a prompt does not necessarily need to contain information that is present in other prompts.

[0050] Furthermore, the prompt may include information other than that described above. For example, the prompt may include at least one of the following: role information, appearance feature information, paint specification information, and component information.

[0051] The role information includes information describing the role of language model 121 (what language model 121 should do when creating response information). Role information may include, for example, information instructing the inspector to inspect the extent of damage to the inspection target image 500 (inspection target area) as a bridge inspector. Furthermore, if the inspection target image 500 is tagged with first to nth tags, the role information may include, for example, information instructing the inspector to use the first to nth tags to identify the areas where the extent of damage has been evaluated.

[0052] Furthermore, role information may include, for example, information specifying the language to be used for responses. Furthermore, role information may include, for example, information that does not need to be considered when creating response information. Information that does not need to be considered when creating response information may include at least one of the following: information about repairing damage and information about the impact of damage on the bridge structure. Also, role information may include, for example, information that should not be mentioned when describing findings on the (degree) of damage to the inspected part. Such information may include at least one of the following: information about repairing damage, information about the impact of damage on the bridge structure, and tag information (number). Furthermore, the information included in the role information may also be included in the prompt as information independent of the role information.

[0053] Appearance feature information is information that describes the appearance features of at least one of the parts being inspected and / or the damage. Appearance feature information may also be information that describes how the inspection image 500 was taken. Painting specification information is information that describes the painting specifications of the part being inspected.

[0054] Member information is information that describes the members of a bridge. In member information, the members of the bridge may be described by the name of the member (i.e., only the name of the member of the bridge may be included in the member information). In addition, the member information may include text that describes the characteristics of the member of the bridge. Members of a bridge include, for example, members used to constitute at least a part of the area to be inspected. If the area to be inspected is space, the members of the bridge include, for example, members that constitute the outline of the space that is the area to be inspected, and members that are installed in at least a part of the area of ​​the space that is the area to be inspected. Members of a bridge include, for example, at least one of main girders, cross girders, bracing, transverse bracing, bearings, expansion joints, piers, abutments, and foundations.

[0055] Furthermore, the above information may be included as part of other information. For example, in the appearance feature information, the text describing the appearance features of the part to be inspected may include at least one of the following: material information, painting specification information, and component information.

[0056] Furthermore, the prompt may include output format information. This output format information may include data (metadata) that indicates the specifications of the response information.

[0057] Furthermore, for example, the bridge inspection device 110 may include a feature information calculation unit that calculates feature information of the part to be inspected included in the inspection target image 500 using an artificial intelligence model. The feature information of the part to be inspected includes additional information (for example, at least one of the material of the part to be inspected, the painting specifications of the part to be inspected, and the bridge members). For example, a CNN (Convolutional Neural Network) may be used as such an artificial intelligence model. In this case, the prompt acquisition unit 111 may acquire a prompt corresponding to the additional information included in the feature information calculated by the feature information calculation unit. Also, in this case, it is not necessary to specify the additional information in the additional information specification field 412 on the inspection screen 410 before inspection. For example, the additional information specification field 412 does not need to be displayed on the inspection screen 410 before inspection.

[0058] <Metadata acquisition unit 113> The metadata acquisition unit 113 acquires metadata that indicates the specifications of the response information. By using the metadata, the language model 121 can be made to create the response information in the specifications (format) indicated by the metadata. For example, when using GPT 4-o (registered trademark) as the language model, the metadata may include information that defines each argument output by the language model 121 using the function calling function. Figure 9 is a diagram showing an example of metadata 900. Figure 9 illustrates a case in which the language model 121 is made to create response information including damage degree information, tag information, and observation information, which are created according to the specifications 901 for damage degree information, the specifications 902 for tag information (number), and the specifications 903 for observation information, using data based on a JSON schema. Here, the observation information is information that describes the findings of the damage (degree) to the part being inspected.

[0059] The operator, for example, performs an input operation on the input device to input the contents of metadata 900 into the bridge inspection device 110. In this case, the metadata acquisition unit 113 stores the metadata 900 input by the input operation. When the inspection execution instruction button 416 is pressed, the metadata acquisition unit 113 acquires the metadata 900 by reading it from the stored data. The storage medium for storing the metadata 900 may be located inside or outside the bridge inspection device 110.

[0060] <Reference image acquisition unit 114> In Figure 2, the reference image acquisition unit 114 acquires reference image data. The reference image acquisition unit 114 acquires reference image data by, for example, reading a file of reference image data that is specified to be referenced in a prompt acquired by the prompt acquisition unit 111 from among the reference image data files stored on the storage medium. The storage medium may be located inside or outside the bridge inspection device 110.

[0061] <Input section 117> The input unit 117 inputs information necessary for the language model 121 to determine the extent of bridge damage. The input unit 117 may also input information to the language model 121 via an API (Application Programming Interface) or the like. In this embodiment, an example is given where the input unit 117 includes a prompt input unit 117a, an inspection target image input unit 117b, a metadata input unit 117c, and a reference image input unit 117d.

[0062] The prompt input unit 117a inputs a prompt containing damage criterion information to the language model 121. In this embodiment, an example is given in which the prompt input unit 117a inputs a prompt acquired by the prompt acquisition unit 111 to the language model 121.

[0063] The inspection target image input unit 117b inputs image data corresponding to bridge damage (inspection target image data) to the language model 121. In this embodiment, the inspection target image input unit 117b inputs inspection target image data to which tag information has been assigned by the tag information assignment unit 115 (data of the inspection target image 500 to which tag information has been assigned) to the language model 121.

[0064] The metadata input unit 117c inputs metadata to the language model 121. In this embodiment, an example is shown where the metadata input unit 117c inputs metadata 900 acquired by the metadata acquisition unit 113 to the language model 121.

[0065] The reference image input unit 117d inputs reference image data corresponding to each degree of damage to the language model 121. In this embodiment, an example is given in which the reference image input unit 117d inputs the reference image data file acquired by the reference image acquisition unit 114 to the language model 121.

[0066] The prompt may include at least one of the following: the image data to be inspected, metadata, and reference image data. In this case, the prompt input unit 117a may have at least one of the following functions: the image data to be inspected input unit 117b, the metadata input unit 117c, and the reference image input unit 117d. That is, at least one of the image data to be inspected input unit 117b, the metadata input unit 117c, and the reference image input unit 117d may be included in the prompt input unit 117a.

[0067] <Answer acquisition section 118> The response acquisition unit 118 acquires response information from the language model 121, which includes damage degree information indicating the degree of damage corresponding to the image data to be inspected. The response acquisition unit 118 may also acquire response information from the language model 121 via an API or the like. The response information includes damage degree information indicating the degree of damage to the part to be inspected, corresponding to the image data to be inspected. In addition, the response information may include findings information that explains the findings regarding the damage (degree) to the part to be inspected, which may be associated with the damage degree information. The association of multiple pieces of information may be done, for example, by assigning common information (key information) to the multiple pieces of information, or the items of the multiple pieces of information may be included in an item of one piece of response information, and the information of that item may be considered as the multiple pieces of information. When using the metadata 900 exemplified in Figure 9, the response information 1000 includes damage degree information 1001, tag information 1002, and findings information 1003, as exemplified in Figure 10. Tag information 1002 is tag information corresponding to the area determined to be damaged by the language model 121 from the first to the Nth tag information.

[0068] <Display Control Unit 119> The display control unit 119 displays information based on the answer information acquired by the answer acquisition unit 118 on the display unit 140. In the following description, the information based on the answer information will be referred to as inspection result information as needed. In this embodiment, an example is given in which the display control unit 119 displays the inspection result information on the inspection screen. Figure 4B is a diagram showing an example of the inspection screen 420 after the inspection.

[0069] Figure 4B illustrates a case where, in addition to the information exemplified in Figure 4A, the inspection screen 420 after the inspection displays the damage extent information display field 421, the findings information display field 422, and the inspection result image display field 423. For the sake of explanation, in Figures 4A and 4B, the inspection screen 410 before the inspection and the inspection screen 420 after the inspection are shown as separate images and assigned different codes (410, 420). However, the inspection screen 420 after the inspection may also be the pre-inspection screen 410 with the damage extent information display field 421, the findings information display field 422, and the inspection result image display field 423 added to it. In other words, instead of the pre-inspection screen 410 switching to the post-inspection screen 420, the damage extent information display field 421, the findings information display field 422, and the inspection result image display field 423 may be displayed to the right of the pre-inspection screen 410. Furthermore, the pre-inspection screen 410 shown in Figure 4A may also display the information items that will be displayed on the post-inspection screen 420 ("Degree of damage:", "Description of damage:", and the frame for the inspection result image display area 423).

[0070] Damage extent information display area 421 displays information about the extent of the damage. The findings information display field 422 displays the findings information. The inspection result image display area 423 displays information corresponding to the degree of damage for each of the 1st to Nth regions of the image being inspected.

[0071] In this embodiment, the display control unit 119 identifies a region 423a in the image to be inspected 500 that has been determined to be damaged by the language model 121, based on the tag information 1002 included in the response information acquired by the response acquisition unit 118, and displays the region 423a in color. In this case, the region that the language model 121 has determined to be undamaged is displayed as the image to be inspected (itself) as information corresponding to the degree of damage. That is, in the inspection result image display field 423, the information of the region that the language model 121 has determined to be undamaged is the same as the information displayed in the image to be inspected display field 415. As mentioned above, the first to Nth regions and the first to Nth tag information are mutually associated. Therefore, the display control unit 119 may, for example, identify a region 423a in the image to be inspected 500 that the language model 121 has determined to be damaged, based on this association, from the tag information 1002 included in the response information. As described above, Figure 4B illustrates a case where the information displayed in the damage extent information display area 421, the findings information display area 422, and the examination result image display area 423 is examination result information.

[0072] <Bridge Inspection Methods> Next, an example of a bridge inspection method performed using the bridge inspection device 110 of this embodiment will be described with reference to the flowchart in Figure 11. For the sake of simplicity, it will be assumed that the prompt to be acquired, the image to be inspected, and the metadata to be acquired by the prompt acquisition unit 111, the inspection target image acquisition unit 112, and the metadata acquisition unit 113 are already stored in the storage medium before the flowchart in Figure 11 is started.

[0073] In the first step S1101, the display control unit 119 displays the pre-inspection screen 410 on the display unit 140 (see Figure 4A). The operator then performs input operations on the bridge inspection device 110, specifying the type of damage, additional information, and the file of the image data to be inspected in the damage type specification field 411, the additional information specification field 412, and the image data to be inspected specification field 413.

[0074] Next, in step S1102, the inspection target image acquisition unit 112 acquires the inspection target image data file specified in the inspection target image specification field 413. The display control unit 119 also displays summary information (file name and file format, etc.) of the inspection target image data file in the inspection target image specification field 413. The display control unit 119 also displays the inspection target image indicated by the inspection target image data in the inspection target image display field 415.

[0075] Next, in step S1103, the tag information assignment unit 115 assigns the first to the Nth tag information to the image 500 to be inspected. Next, in step S1104, the operation reception unit 116 waits until the inspection execution instruction button 416 is pressed.

[0076] Then, when the inspection execution instruction button 416 is pressed (if YES is selected in step S1104), the process in step S1105 is performed. In step S1105, the prompt acquisition unit 111 acquires prompts corresponding to the type of damage specified in the damage type specification field 411 and the additional information specified in the additional information specification field 412.

[0077] Next, in step S1106, the reference image acquisition unit 114 acquires the reference image data file that is specified to be referenced in the prompt acquired in step S1105. If the prompt acquired in step S1105 does not specify that a reference image data file be referenced, the processing in step S1106 is omitted. Next, in step S1107, the metadata acquisition unit 113 acquires the metadata 900.

[0078] Next, in step S1108, the prompt input unit 117a, the inspection target image input unit 117b, the metadata input unit 117c, and the reference image input unit 117d each input the prompt obtained in step S1105, the inspection target image data file to which tag information was added in step S1103, the reference image data obtained in step S1106, and the metadata 900 obtained in step S1107, respectively, into the language model 121.

[0079] Next, in step S1109, the response acquisition unit 118 acquires response information 1000 from the language model 121. Finally, in step S1110, the display control unit 119 displays the inspection result information on the inspection screen 420 after the inspection (damage degree information display field 421, findings information display field 422, and inspection result image display field 423).

[0080] <Examples of prompts> Next, I will explain a specific example of a prompt. Figures 12A to 12C and 13A to 13B illustrate specific examples of differences in prompts due to differences in corrosion protection specifications of the materials constituting the components. Figures 12A to 12C show an example of prompt 1200 when the material constituting the member is ordinary steel. The prompt 1200 shown in Figures 12A to 12C includes role information 1201, damage definition information 1202, appearance feature information 1203, damage criterion information 1204, output format information 1205, and judgment example information 1206. Furthermore, the damage criterion information 1204 includes damage range information 1204a to 1204d and damage depth information 1204e to 1204h. Note that the specific contents of judgment example information 1206 are omitted in Figures 12A to 12C.

[0081] Figures 13A and 13B show an example of prompt 1300 when the material constituting the member is weathering steel. The prompt 1300 shown in Figures 13A to 13B includes damage definition information 1301, appearance feature information 1302, damage criterion information 1303, output format information 1304, and judgment example information 1305. The damage criterion information 1303 also includes damage range information 1303a to 1303d and damage depth information 1303e to 1303h. In Figures 13A to 13B, the notation of role information is omitted, but prompt 1300 also includes role information. In addition, in Figures 13A to 13B, some notation of damage definition information 1303 is omitted, as well as the specific contents of output format information 1304 and judgment example information 1305.

[0082] Corrosion in weathering steel can occur in different forms than corrosion in other steel materials. Therefore, Figures 13A and 13B illustrate how to include explanations specific to weathering steel in the prompt 1300 for the definition of corrosion (damage definition information 1301), the visual characteristics of the corrosion (visual characteristics information 1302), and the criteria for the degree of damage (damage criteria information 1303) (see explanations 1301a, 1302a-1302b, and 1303a). This allows for input of more detailed knowledge about corrosion in weathering steel into the language model 121, thereby prompting the language model 121 to understand corrosion in weathering steel.

[0083] Next, Figures 14A to 14C and Figure 15 illustrate specific examples of differences in prompts due to differences in components. Figures 14A to 14C show a first example of prompt 1400 when the part to be inspected is a gap. Figures 14A to 14C illustrate prompt 1400 used when determining the degree of damage to the gap in an expandable device using an image of the part to be inspected, including the expandable device.

[0084] The prompt 1400 shown in Figures 14A to 14C includes role information 1401, damage definition information 1402, appearance feature information 1403, damage criterion information 1404, output format information 1405, and judgment example information 1406. In Figures 14A to 14C, the judgment example information 1406 shows the images to be inspected 1406a to 1406b, but as mentioned above with reference to Figures 8A to 8H, the storage location (path name, etc.) of the images to be inspected 1406a to 1406b may also be included in the judgment example information 1406 (this is also the case in Figure 15).

[0085] Figure 15 shows a second example of prompt 1500 when the part to be inspected is a gap. Figure 15 illustrates prompt 1500 used when determining the degree of damage to the gap in the main girder using an inspection image that includes the main girder. The prompt 1500 shown in Figure 15 includes judgment example information 1501. Note that in Figure 15, the notation of role information, damage definition information, appearance characteristic information, damage criterion information, and output format information is omitted, but prompt 1500 also includes this information. As illustrated in Figures 14C and 15, the morphology of the gap between playpens varies greatly depending on the location. Therefore, by inputting reference image data into the language model 121, it is possible to prompt the language model 121 to understand the damage to the gap between playpens.

[0086] Next, Figures 16A to 16B and 17A to 17B illustrate specific examples of differences in prompts due to differences in the material of the part being inspected. Figures 16A and 16B show a first example of prompt 1600 when the part to be inspected is a support part. Figures 16A and 16B show an example of prompt 1600 when the support part is made of steel. The prompt 1600 shown in Figures 16A to 16B includes role information 1601, damage definition information 1602, appearance feature information 1603, damage criterion information 1604, output format information 1605, and judgment example information 1606. Note that the specific contents of judgment example information 1606 are omitted in Figures 16A to 16B.

[0087] Figures 17A and 17B show a second example of prompt 1700 when the part to be inspected is a support part. Figures 17A and 17B show an example of prompt 1600 when the support part is made of rubber. The prompt 1700 shown in Figures 17A to 17B includes damage definition information 1701, appearance feature information 1702, damage criterion information 1703, output format information 1704, and judgment example information 1705. Note that the role information is omitted in Figures 17A to 17B, but the prompt 1700 also includes role information. Furthermore, the specific contents of the output format information 1704 and judgment example information 1705 are omitted in Figures 17A to 17B.

[0088] As illustrated in Figures 17A to 17B, the prompt 1700 when the part to be inspected is a rubber support part includes different explanations 1701a to 1701c than the prompt 1600 when the part to be inspected is an iron support part. As illustrated in Figures 16A-16B and 17A-17B, by inputting detailed information about the materials of the components constituting the part to be inspected into the language model 121, it is possible to promote an understanding of each material.

[0089] <Summary> In this embodiment, the bridge inspection device 110 inputs a prompt containing damage criterion information explaining the criteria for the degree of damage to a bridge, and image data to be inspected, into the language model 121, and obtains response information from the language model 121 that includes damage degree information indicating the degree of damage corresponding to the image to be inspected. Therefore, the degree of damage to a bridge can be determined automatically and with high accuracy. Furthermore, by using the language model 121, it is possible to obtain response information similar to that obtained when an experienced inspector performs an inspection. In addition, by using the language model 121, even if the judgment criteria are changed or the judgment accuracy is improved, as described in Non-Patent Document 1 above, it becomes unnecessary to update the trained model itself.

[0090] Furthermore, in this embodiment, the prompt may include damage definition information that explains the definition of the type of damage. Also, in this embodiment, the prompt may include information that explains that it is not necessary to consider information about repairing the damage and information about the impact of the damage on the bridge structure when creating the response information. Also, in this embodiment, the prompt may include at least one of material information that explains the material of the bridge and paint specification information that explains the specifications of the paint on the bridge. Also, in this embodiment, the prompt may include member information that explains the members of the bridge. The more information included in the prompt as this information, the higher the accuracy of the response information can be.

[0091] Furthermore, in this embodiment, the prompts may include prompts for at least one of the bridge material and the bridge painting specifications. The prompts may also include prompts for each bridge component. This allows for the use of prompts appropriate to the inspection objective (bridge material, painting specifications, and components). Therefore, the accuracy of the response information can be further improved.

[0092] Furthermore, in this embodiment, the bridge inspection device 110 may determine the degree of bridge corrosion as the degree of damage to the bridge. By doing so, the bridge inspection device 110 inputs a prompt to the language model 121 that includes damage criterion information explaining the criteria for the degree of corrosion, so that it can more reliably obtain response information about the degree of corrosion damage from the language model 121.

[0093] Furthermore, in this embodiment, when the bridge inspection device 110 determines the degree of corrosion of the bridge, it may include damage range information in the damage criterion information that explains the criteria for the extent of the damage. By doing so, the bridge inspection device 110 can more reliably obtain response information corresponding to the extent of the damage (corrosion) from the language model 121. Furthermore, in this embodiment, when the bridge inspection device 110 determines the degree of corrosion of the bridge, it may include damage depth information in the damage criterion information that explains the criteria for the depth of the damage. By doing so, the bridge inspection device 110 can more reliably obtain response information corresponding to the depth of the damage (corrosion) from the language model 121.

[0094] Furthermore, in this embodiment, the bridge inspection device 110 may acquire response information that includes findings information describing the findings of the damage. By doing so, the bridge inspection device 110 can acquire more detailed information about the extent of the damage more reliably. In this case, the bridge inspection device 110 may acquire response information in which the extent of damage information and the findings information are correlated with each other. By doing so, the bridge inspection device 110 and the operator can more easily grasp the correspondence between the extent of damage information and the findings information.

[0095] Furthermore, in this embodiment, the bridge inspection device 110 may acquire structured response information. By doing so, the bridge inspection device 110 can more reliably acquire response information with certain specifications.

[0096] Furthermore, in this embodiment, the bridge inspection device 110 may input metadata indicating the specifications of the response information into the language model 121. By doing so, the bridge inspection device 110 can more reliably obtain response information that conforms to the presented specifications.

[0097] Furthermore, in this embodiment, the bridge inspection device 110 may include in the image 500 to be inspected first tag information corresponding to a first region of the image 500 and second tag information corresponding to a second region of the image 500 to be inspected. By doing so, the language model 121 can recognize the region for which the degree of damage is to be determined more easily and reliably. In addition, the bridge inspection device 110 can acquire the degree of damage for each of the multiple regions of the image 500 to be inspected as response information.

[0098] Furthermore, in this embodiment, the prompt may include a prompt corresponding to the first tag information and a prompt corresponding to the second tag information. By doing so, prompts corresponding to multiple regions (images) of the image 500 to be inspected can be used. Thus, the accuracy of the response information can be further improved.

[0099] Furthermore, in this embodiment, the bridge inspection device 110 may display information corresponding to the degree of damage information included in the response information in the display unit 140, for each of the first and second regions of the inspection target image 500. In this way, the operator can more easily grasp the response information obtained from the language model 121.

[0100] Furthermore, in this embodiment, the prompt may include information instructing the language model 121 to respond using the first tag information and the second tag information, indicating the area where the degree of damage has been assessed. In this way, the bridge inspection device 110 can more easily and reliably grasp the area where the degree of damage has been assessed by the language model 121.

[0101] Furthermore, in this embodiment, the bridge inspection device 110 may input reference image data corresponding to each of the multiple degrees of damage to the language model 121. Doing so can further improve the accuracy of the response information from the language model 121.

[0102] Furthermore, in this embodiment, the bridge inspection device 110 may determine the degree of damage to the bridge as at least one of the following: water leakage / free lime, spalling / reinforcement exposure, and delamination. By doing so, the bridge inspection device 110 inputs prompts containing damage criterion information explaining the criteria for these damage degrees into the language model 121, thereby more reliably obtaining response information about various damage degrees from the language model 121.

[0103] (Other embodiments) Furthermore, the embodiments of this disclosure described above can be realized by a computer executing a program. A computer-readable recording medium on which the program is recorded, and computer program products such as the program itself, can also be applied as embodiments of this disclosure. Examples of recording media that can be used include flexible disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, magnetic tapes, non-volatile memory cards, ROMs, and the like. Furthermore, the embodiments of this disclosure described above are merely examples of concrete implementations of this disclosure, and the technical scope of this disclosure should not be interpreted as being limited by them. In other words, this disclosure can be implemented in various ways without departing from its technical concept or its main features.

[0104] Furthermore, the disclosure of the above embodiments is as follows, for example. [Disclosure 1] A prompt input step involves inputting a prompt into a language model that includes damage criteria information describing the criteria for the degree of damage to a bridge, An inspection target image input step involves inputting image data corresponding to the damage into the language model, A response acquisition step of obtaining response information from the language model, which includes damage degree information indicating the degree of damage corresponding to the image data, A bridge inspection method characterized by comprising the following features. [Disclosure 2] The prompt includes damage definition information that describes the definition of the type of damage, The bridge inspection method described in Disclosure 1, characterized by the features described herein. [Disclosure 3] The prompt includes information explaining that, when creating the response information, it is not necessary to consider information regarding the repair of the damage and information regarding the impact of the damage on the structure of the bridge. A bridge inspection method according to disclosure 1 or 2, characterized by the features described herein. [Disclosure 4] The prompt includes at least one of material information describing the material of the bridge and painting specification information describing the painting specifications of the bridge. A bridge inspection method according to any one of disclosures 1 to 3, characterized by the features described herein. [Disclosure 5] The prompt includes a prompt for at least one of the materials of the bridge and the specifications for the painting of the bridge. A bridge inspection method according to any one of disclosures 1 to 4, characterized by the features described herein. [Disclosure 6] The prompt includes member information describing the members of the bridge, A bridge inspection method according to any one of disclosures 1 to 5, characterized by the features described herein. [Disclosure 7] The prompt includes a prompt for each member of the bridge. A bridge inspection method according to any one of disclosures 1 to 6, characterized by the features described herein. [Disclosure 8] The damage includes corrosion. A bridge inspection method according to any one of disclosures 1 to 7, characterized by the features described herein. [Disclosure 9] The damage criteria information includes damage extent information that describes the criteria for the extent of the damage, The bridge inspection method according to disclosure 8, characterized by the features described above. [Disclosure 10] The damage criteria information includes damage depth information that describes the criteria for the depth of the damage. A bridge inspection method according to disclosure 8 or 9, characterized by the features described above. [Disclosure 11] The aforementioned response information includes findings information that explains the findings of the aforementioned damage. A bridge inspection method according to any one of disclosures 1 to 10, characterized by the features described herein. [Disclosure 12] The damage extent information is associated with the findings information in the response information. The bridge inspection method described in disclosure 11, characterized by the features described herein. [Disclosure 13] The aforementioned response information is structured. A bridge inspection method according to any one of disclosures 1 to 12, characterized by the features described herein. [Disclosure 14] A metadata input step of inputting metadata indicating the specifications of the response information into the language model, A bridge inspection method according to any one of disclosures 1 to 13, further comprising the above. [Disclosure 15] The image data includes first tag information corresponding to a first region included in the image shown by the image data, and second tag information corresponding to a second region included in the image. A bridge inspection method according to any one of disclosures 1 to 14, characterized by the features described herein. [Disclosure 16] The prompt includes the prompt corresponding to the first tag information and the prompt corresponding to the second tag information. The bridge inspection method according to disclosure 15, characterized by the features described herein. [Disclosure 17] A display control step of displaying an image corresponding to the aforementioned image data on a display unit. Furthermore, In the display control step, information corresponding to the degree of damage is displayed in the first region and the second region, respectively. A bridge inspection method according to disclosure 15 or 16, characterized by the features described herein. [Disclosure 18] The prompt includes information instructing the language model to respond using the first tag information and the second tag information, regarding the region where the degree of damage has been assessed. A bridge inspection method according to any one of disclosures 15 to 17, characterized by the features described herein. [Disclosure 19] A reference image input step involves inputting reference image data corresponding to each of the multiple degrees of damage into the language model. A bridge inspection method according to any one of disclosures 1 to 18, further comprising the above. [Disclosure 20] The damage includes damage caused by at least one of the following: water leakage / free lime, spalling / reinforcement exposure, and budding. A bridge inspection method according to any one of disclosures 1 to 19, characterized by the features described herein. [Disclosure 21] A prompt input unit that inputs a prompt to a language model, which includes damage criteria information explaining the criteria for the degree of damage to a bridge, An inspection target image input unit inputs image data corresponding to the damage to the language model, A response acquisition unit that acquires response information from the language model, which includes damage degree information indicating the degree of damage corresponding to the image data, A bridge inspection device characterized by being equipped with the following features. [Disclosure 22] A program for causing a computer to perform each step of the bridge inspection method described in any one of disclosures 1 to 20. [Explanation of symbols]

[0105] 110 Bridge Inspection Device 111 Prompt acquisition section 112 Image acquisition unit for the subject of inspection 113 Metadata Acquisition Unit 114 Reference image acquisition section 115 Tag information assignment unit 116 Operation Reception Unit 117 Input section 117a Prompt input section 117b Image input section for the image to be inspected 117c Metadata Input Section 117d Reference Image Input Section 118 Answer acquisition part 119 Display Control Unit 120 Language Model Servers 130 Networks 140 Display section 410 Pre-examination screen 411 Damage type specification field 412 Additional information specification field 413 Field for specifying the image to be examined 414 Display field for image files to be inspected 415 Display area for images to be inspected 416 Test execution command button 420 Screen after the test 421 Damage degree information display column 422 Findings information display column 423 Inspection Result Image Display Area 423a Area determined to be damaged 500 images to be examined 511a~511d Hand-drawn linear patterns 512a~512c Handwritten text 600 tagged images to be inspected 800 Prompts 810a~810d Damage Type Information 820a~820d Material Information 830a~830d Damage definition information Damage Criteria Information 840a~840d 850a~850c Judgment Example Information 900 metadata 901 Damage Severity Information Specifications 902 Tag Information (Number) Specifications 903 Specifications of findings information 1000 answer information 1001 Damage degree information 1002 Tag Information (Number) 1003 Findings Information 1200 prompt 1201 Role Information 1202 Damage definition information 1203 Exterior Features Information 1204 Damage criteria information 1205 Output format information 1206 Example of Judgment Information 1300 prompt 1301 Damage definition information 1302 Exterior Features Information 1303 Damage standard information 1304 Output format information 1305 Case Examples Information 1400 prompt 1401 Role Information 1402 Damage definition information 1403 Exterior Features Information 1404 Damage standard information 1405 Output format information 1406 Case Examples Information 1500 prompt 1501 Judgment Example Information 1600 prompt 1601 Role Information 1602 Damage definition information 1603 Exterior Features Information 1604 Damage standard information 1605 Output format information 1606 Case Examples Information 1700 Prompt 1701 Damage definition information 1702 Exterior Features Information 1703 Damage standard information 1704 Output format information 1705 Case Example Information

Claims

1. A prompt input step involves inputting a prompt into a language model that includes damage criteria information describing the criteria for the degree of bridge damage, An inspection target image input step involves inputting image data corresponding to the damage into the language model, A response acquisition step of obtaining response information from the language model, which includes damage degree information indicating the degree of damage corresponding to the image data, A bridge inspection method characterized by comprising the following features.

2. The prompt includes damage definition information that describes the definition of the type of damage, The bridge inspection method according to feature 1.

3. The prompt includes information explaining that, when creating the response information, it is not necessary to consider information regarding the repair of the damage and information regarding the impact of the damage on the structure of the bridge. The bridge inspection method according to feature 1 or 2.

4. The prompt includes at least one of material information describing the material of the bridge and painting specification information describing the painting specifications of the bridge. The bridge inspection method according to feature 1 or 2.

5. The prompt includes a prompt for at least one of the materials of the bridge and the specifications for the painting of the bridge. The bridge inspection method according to feature 1 or 2.

6. The prompt includes member information describing the members of the bridge, The bridge inspection method according to feature 1 or 2.

7. The prompt includes a prompt for each member of the bridge. The bridge inspection method according to feature 1 or 2.

8. The damage includes corrosion. The bridge inspection method according to feature 1 or 2.

9. The damage criteria information includes damage extent information that describes the criteria for the extent of the damage, The bridge inspection method according to feature 8.

10. The damage criteria information includes damage depth information that describes the criteria for the depth of the damage. The bridge inspection method according to feature 8.

11. The aforementioned response information includes findings information that explains the findings of the aforementioned damage. The bridge inspection method according to feature 1 or 2.

12. The damage extent information is associated with the findings information in the response information. The bridge inspection method according to feature 11.

13. The aforementioned response information is structured. The bridge inspection method according to feature 1 or 2.

14. A metadata input step of inputting metadata indicating the specifications of the response information into the language model, The bridge inspection method according to claim 1 or 2, further comprising the following:

15. The image data includes first tag information corresponding to a first region included in the image shown by the image data, and second tag information corresponding to a second region included in the image. The bridge inspection method according to feature 1 or 2.

16. The prompt includes the prompt corresponding to the first tag information and the prompt corresponding to the second tag information. The bridge inspection method according to feature 15.

17. A display control step of displaying an image corresponding to the aforementioned image data on a display unit. Furthermore, In the display control step, information corresponding to the degree of damage is displayed in the first region and the second region, respectively. The bridge inspection method according to feature 15.

18. The prompt includes information instructing the language model to respond using the first tag information and the second tag information, regarding the region where the degree of damage has been assessed. The bridge inspection method according to feature 15.

19. A reference image input step involves inputting reference image data corresponding to each of the multiple degrees of damage into the language model. The bridge inspection method according to claim 1 or 2, further comprising the above.

20. The damage includes damage caused by at least one of the following: water leakage / free lime, peeling / exposed rebar, and floating. The bridge inspection method according to feature 1 or 2.

21. A prompt input unit that inputs a prompt containing damage criteria information explaining the criteria for the degree of bridge damage to a language model, An inspection target image input unit inputs image data corresponding to the damage to the language model, A response acquisition unit that acquires response information from the language model, which includes damage degree information indicating the degree of damage corresponding to the image data, A bridge inspection device characterized by being equipped with the following features.

22. A prompt input step involves inputting a prompt into a language model that includes damage criteria information describing the criteria for the degree of bridge damage, An inspection target image input step involves inputting image data corresponding to the damage into the language model, A response acquisition step of obtaining response information from the language model, which includes damage degree information indicating the degree of damage corresponding to the image data, A program that causes a computer to execute something.

Images