Coating evaluation apparatus and coating evaluation method

Abstract

This coating evaluation device and coating evaluation method acquires material information indicating the material of a coating surface, shape information indicating the curved shape of the coating surface, or surface roughness information indicating the surface roughness of the coating surface. Furthermore, an evaluation model that outputs an evaluation value indicating the clarity of the coating surface in response to input including the material information, the shape information, and the surface roughness information is used to estimate an evaluation value corresponding to the combination of the material information, the shape information, and the surface roughness information.

Description

Technical Field

[0001] The present invention relates to a painting evaluation apparatus and a painting evaluation method.

Background Art

[0002] There is known an invention in which, among the surface undulations of a paint film, the amplitude of undulations with a wavelength of 1 mm to 10 mm is selectively measured, and the appearance of the paint film surface is evaluated based on the magnitude of this measurement result (Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] According to the invention described in Patent Document 1, for example, when the painted surface is a curved surface, an error may occur in the amplitude of the undulations to be selectively measured, and there is a problem that the accuracy in evaluating the distinctness of image of the painted surface may decrease.

[0005] The present invention has been made in view of the above problems, and an object thereof is to provide a painting evaluation apparatus and a painting evaluation method capable of accurately evaluating the distinctness of image of a painted surface.

Means for Solving the Problems

[0006] A painting evaluation apparatus and a painting evaluation method according to an aspect of the present invention acquire material information representing the material of a painted surface, shape information representing the curved shape of the painted surface, or surface roughness information representing the surface roughness of the painted surface. Then, an evaluation value corresponding to a combination of the material information, the shape information, and the surface roughness information is estimated using an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including the material information, the shape information, and the surface roughness information.

Effects of the Invention

[0007] According to the present invention, the distinctness of image of a painted surface can be accurately evaluated.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Embodiments for Carrying Out the Invention

[0009] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the description of the drawings, the same parts are denoted by the same reference numerals and the description thereof will be omitted.

[0010] [Configuration of Painting Evaluation Apparatus] Referring to FIG. 1, a configuration example of the painting evaluation apparatus according to the present embodiment will be described. As shown in FIG. 1, the painting evaluation apparatus includes a shape acquisition unit 11, a surface roughness acquisition unit 17, and a controller 100. In addition, the painting evaluation apparatus may include a material acquisition unit 13, an image acquisition unit 21, and an output unit 400. The shape acquisition unit 11, the material acquisition unit 13, the surface roughness acquisition unit 17, the image acquisition unit 21, and the output unit 400 are connected to the controller 100.

[0011] The shape acquisition unit 11 acquires shape information representing the curved shape of the painted surface to be evaluated for painting. More specifically, the shape acquisition unit 11 may acquire design data related to the shape of the painted surface as shape information. For example, CAD (Computer-aided design) data can be cited as the design data. The design data is not limited to this as long as it represents the degree of curvature of the painted surface and the degree of inclination of the painted surface with respect to the surface roughness acquisition unit described later.

[0012] The shape acquisition unit 11 may acquire the stored design data of the painted surface from a database (not shown), or may acquire the design data of the painted surface from an external connection device (not shown) via a wired or wireless network. Alternatively, the shape acquisition unit 11 may acquire the design data based on user input.

[0013] Further, the shape acquisition unit 11 may acquire the measurement data obtained by measuring the painted surface as shape information. For example, various devices such as a 3D scanner may be used as the shape acquisition unit 11.

[0014] The material acquisition unit 13 acquires material information representing the material of the painted surface. More specifically, as the material information of the painted surface, the material acquisition unit 13 acquires design data such as the type of material constituting the painted surface, the film thickness (coating thickness) for each layer constituting the painted surface, optical properties (material reflectance, material transmittance, material refractive index), lightness, chroma, and hue. Alternatively, the material acquisition unit 13 may acquire information such as the amount, shape, and orientation of the brightening material contained in the coating as the material information of the painted surface.

[0015] The material acquisition unit 13 may acquire the stored material information of the painted surface from a database (not shown), or may acquire the material information of the painted surface from an external connection device (not shown) via a wired or wireless network. Alternatively, the material acquisition unit 13 may acquire the material information based on user input.

[0016] Further, the material acquisition unit 13 may acquire, as material information, the measurement data such as the film thickness, optical properties, lightness, chroma, and hue for each layer constituting the painted surface, obtained by measuring the painted surface. For example, various devices such as a film thickness gauge, a spectrophotometer, a color difference meter, a reflectance meter, and a refractometer may be used as the material acquisition unit 13.

[0017] The surface roughness acquisition unit 17 acquires surface roughness information representing the surface roughness of the painted surface. More specifically, the surface roughness acquisition unit 17 may acquire the surface roughness obtained by measuring the painted surface as the surface roughness information. For example, a laser microscope can be cited as the surface roughness acquisition unit 17.

[0018] In addition, the surface roughness acquisition unit 17 may acquire the stored surface roughness information of the painted surface from a database (not shown), or may acquire the surface roughness information of the painted surface from an external connection device (not shown) via a wired or wireless network. In addition, the shape acquisition unit 11 may acquire the surface roughness information based on the input of the user.

[0019] The image acquisition unit 21 acquires a captured image of the painted surface to be the object of painting evaluation. More specifically, the image acquisition unit 21 is a digital camera equipped with a solid-state imaging device such as a CCD or a CMOS, and captures the painted surface to acquire a digital image.

[0020] The image acquisition unit 21 captures the painted surface to be the object of painting evaluation by setting the focal length, the angle of view of the lens, the vertical and horizontal angles of the camera, etc.

[0021] The controller 100 is a general-purpose computer including a CPU (Central Processing Unit), a memory, a storage device, an input / output unit, etc.

[0022] A computer program (painting evaluation program) for causing the controller 100 to function as a painting evaluation device is installed in the controller 100. By executing the computer program, the controller 100 functions as a plurality of information processing circuits included in the painting evaluation device.

[0023] Here, an example of realizing a plurality of information processing circuits provided in the painting evaluation apparatus by software is shown. Of course, it is also possible to configure the information processing circuit by preparing dedicated hardware for executing each of the information processes shown below. Further, the plurality of information processing circuits may be configured by individual hardware.

[0024] The controller 100 includes an evaluation model setting unit 120, an evaluation value estimation unit 130, and a position specifying unit 140.

[0025] The evaluation model setting unit 120 sets an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface.

[0026] Here, the evaluation value of the distinctness of image is an index determined by at least one of, for example, the smoothness of the painted surface, the ratio of diffuse reflection in the reflected light on the painted surface, and the resolution of the image reflected on the painted surface. The evaluation value of the distinctness of image of the evaluated painted surface is a numerical value given in advance to the evaluated painted surface by another distinctness of image evaluation method.

[0027] For example, the evaluation model setting unit 120 may set an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including shape information and surface roughness information. In this case, the evaluation model is a learning model generated by machine learning based on teacher data that includes the shape information of the evaluated painted surface, the surface roughness information of the evaluated painted surface, and the evaluation value of the distinctness of image of the evaluated painted surface as a set.

[0028] The evaluation model setting unit 120 may set an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including surface roughness information and material information. In this case, the evaluation model is a learning model generated by machine learning based on teacher data that includes the surface roughness information of the evaluated painted surface, the material information of the evaluated painted surface, and the evaluation value of the distinctness of image of the evaluated painted surface as a set.

[0029] The evaluation model setting unit 120 may set an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including material information and shape information. In this case, the evaluation model is a learning model generated by machine learning based on teacher data that is a set of the material information of the evaluated painted surface, the shape information of the evaluated painted surface, and the evaluation value of the distinctness of image of the evaluated painted surface.

[0030] The evaluation model setting unit 120 may set an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including material information, shape information, and surface roughness information. In this case, the evaluation model is a learning model generated by machine learning based on teacher data that is a set of the material information of the evaluated painted surface, the shape information of the evaluated painted surface, the surface roughness information of the evaluated painted surface, and the evaluation value of the distinctness of image of the evaluated painted surface.

[0031] Examples of methods for generating a learning model by machine learning include, for example, methods using one or a combination of two or more of neural network, support vector machine, Random Forest, XGBoost, LightGBM, PLS regression, Ridge regression, and Lasso regression. The method for generating a learning model by machine learning is not limited to the examples given here.

[0032] The evaluation model setting unit 120 may perform machine learning based on teacher data acquired from a database (not shown) to set an evaluation model. Also, an evaluation model may be stored in advance in a database (not shown), and the evaluation model setting unit 120 may set the evaluation model acquired from the database.

[0033] Note that the evaluation model may be configured by a neural network including an input layer and an output layer. More specifically, a neural network typically has an input layer, a plurality of hidden layers, and an output layer, and each layer (input layer, hidden layer, output layer) includes a plurality of neurons.

[0034] The input layer includes shape information representing the curved shape of the painted surface and surface roughness information representing the surface roughness of the painted surface as input data to be processed through each hidden layer. Also, the neurons in the output layer are assigned an evaluation value of the distinctness of image of the painted surface, which is assigned to the input data by the neural network. That is, the output data output from the output layer is the evaluation value of the distinctness of image of the painted surface.

[0035] The neural network constituting the evaluation model is trained by the evaluation model setting unit 120 to reproduce the given teacher data.

[0036] When teacher data is given as a set including the shape information of the evaluated painted surface, the surface roughness information of the evaluated painted surface, and the evaluation value of the distinctness of image of the evaluated painted surface, the neural network is trained by machine learning so that when input data including the shape information and the surface roughness information is input, the evaluation value of the distinctness of image of the painted surface is output as output data.

[0037] When teacher data is given as a set including the surface roughness information of the evaluated painted surface, the material information of the evaluated painted surface, and the evaluation value of the distinctness of image of the evaluated painted surface, the neural network is trained by machine learning so that when input data including the surface roughness information and the material information is input, the evaluation value of the distinctness of image of the painted surface is output as output data.

[0038] When teacher data is given as a set including the material information of the evaluated painted surface, the shape information of the evaluated painted surface, and the evaluation value of the distinctness of image of the evaluated painted surface, the neural network is trained by machine learning so that when input data including the material information and the shape information is input, the evaluation value of the distinctness of image of the painted surface is output as output data.

[0039] When teacher data is given as a set including the material information of the evaluated painted surface, the shape information of the evaluated painted surface, the surface roughness information of the evaluated painted surface, and the evaluation value of the distinctness of image of the evaluated painted surface, the neural network is trained by machine learning so that when input data including the material information, the shape information, and the surface roughness information is input, the evaluation value of the distinctness of image of the painted surface is output as output data.

[0040] In this way, machine learning based on teacher data is performed to generate an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input.

[0041] The evaluation value estimation unit 130 estimates an evaluation value corresponding to the input using the set evaluation model.

[0042] When the set evaluation model is a learning model generated by machine learning based on teacher data that is a set of shape information of the evaluated painted surface, surface roughness information of the evaluated painted surface, and an evaluation value of the distinctness of image of the evaluated painted surface, the evaluation value estimation unit 130 inputs the shape information and the surface roughness information to the evaluation model. Then, the evaluation value estimation unit 130 sets the value output from the evaluation model as the evaluation value corresponding to the combination of the shape information and the surface roughness information. The evaluation value corresponding to the combination of the shape information and the surface roughness information is an estimated evaluation value regarding the distinctness of image of the painted surface.

[0043] When the set evaluation model is a learning model generated by machine learning based on teacher data that is a set of surface roughness information of the evaluated painted surface, material information of the evaluated painted surface, and an evaluation value of the distinctness of image of the evaluated painted surface, the evaluation value estimation unit 130 inputs the surface roughness information and the material information to the evaluation model. Then, the evaluation value estimation unit 130 sets the value output from the evaluation model as the evaluation value corresponding to the combination of the surface roughness information and the material information. The evaluation value corresponding to the combination of the surface roughness information and the material information is an estimated evaluation value regarding the distinctness of image of the painted surface.

[0044] When the set evaluation model is a learning model generated by machine learning based on teacher data that is a set of material information of the evaluated painted surface, shape information of the evaluated painted surface, and an evaluation value of the distinctness of image of the evaluated painted surface, the evaluation value estimation unit 130 inputs the material information and the shape information to the evaluation model. Then, the evaluation value estimation unit 130 sets the value output from the evaluation model as the evaluation value corresponding to the combination of the material information and the shape information. The evaluation value corresponding to the combination of the material information and the shape information is an estimated evaluation value regarding the distinctness of image of the painted surface.

[0045] When the set evaluation model is a learning model generated by machine learning based on teacher data that includes material information of the evaluated painted surface, shape information of the evaluated painted surface, surface roughness information of the evaluated painted surface, and an evaluation value of the distinctness of image of the evaluated painted surface as a set, the evaluation value estimation unit 130 inputs the shape information, the surface roughness information, and the material information to the evaluation model. Then, the evaluation value estimation unit 130 uses the value output from the evaluation model as the evaluation value corresponding to the combination of the shape information, the surface roughness information, and the material information. The evaluation value corresponding to the combination of the shape information, the surface roughness information, and the material information is the estimated evaluation value regarding the distinctness of image of the painted surface.

[0046] The position specifying unit 140 associates the captured image and the shape information with the surface roughness information and records the position on the painted surface where the surface roughness information was obtained. More specifically, the captured image and the shape information at the time when the surface roughness information was obtained are associated with the surface roughness information and recorded in a database (not shown) or the like. Thereby, the position on the painted surface where the surface roughness information was obtained is specified.

[0047] The position specifying unit 140 may specify the position on the painted surface when the surface roughness information and the material information were obtained by associating the captured image with the position on the painted surface where the surface roughness information and the material information were obtained and recording it.

[0048] The output unit 400 outputs the estimated evaluation value regarding the distinctness of image of the painted surface.

[0049] [Processing Procedure of Painting Evaluation Apparatus] Next, the processing procedure of the painting evaluation apparatus according to the present embodiment will be described with reference to the flowchart of FIG. 2. It is assumed that the evaluation model has already been set by the evaluation model setting unit 120 before the processing shown in the flowchart of FIG. 2 is started.

[0050] In step S102, the surface roughness acquisition unit 17 acquires surface roughness information representing the surface roughness of the painted surface to be evaluated for painting. Additionally, the shape acquisition unit 11 may acquire shape information representing the curved shape of the painted surface. The material acquisition unit 13 may acquire material information representing the material of the painted surface. The image acquisition unit 21 may acquire a captured image of the painted surface.

[0051] In step S104, the position specifying unit 140 associates the captured image and the shape information with the surface roughness information, and records the position on the painted surface where the surface roughness information was acquired, thereby specifying the position on the painted surface when the surface roughness information was acquired.

[0052] Note that the position specifying unit 140 may also specify the position on the painted surface when the surface roughness information and the material information were acquired by associating the captured image and recording the position on the painted surface where the surface roughness information and the material information were acquired.

[0053] In step S111, the evaluation value estimation unit 130 estimates the evaluation value. More specifically, the evaluation value estimation unit 130 calculates the output from the evaluation model corresponding to the input to the evaluation model. Then, the value output from the evaluation model is estimated as the evaluation value corresponding to the input to the evaluation model.

[0054] For example, when the evaluation model is a learning model generated by machine learning based on teacher data that is a set of shape information of the evaluated painted surface, surface roughness information of the evaluated painted surface, and an evaluation value of the distinctness of image of the evaluated painted surface, the evaluation value estimation unit 130 inputs the combination of the shape information and the surface roughness information to the evaluation model. Then, the evaluation value estimation unit 130 estimates the evaluation value corresponding to the combination of the shape information and the surface roughness information.

[0055] When the evaluation model is a learning model generated by machine learning based on teacher data that includes the shape information of the evaluated painted surface, the material information of the evaluated painted surface, and the evaluation value of the distinctness of image of the evaluated painted surface as a set, the evaluation value estimation unit 130 inputs a combination of the shape information and the material information to the evaluation model. Then, the evaluation value estimation unit 130 estimates the evaluation value corresponding to the combination of the shape information and the material information.

[0056] When the evaluation model is a learning model generated by machine learning based on teacher data that includes the shape information of the evaluated painted surface, the surface roughness information of the evaluated painted surface, the material information of the evaluated painted surface, and the evaluation value of the distinctness of image of the evaluated painted surface as a set, the evaluation value estimation unit 130 inputs a combination of the shape information, the surface roughness information, and the material information to the evaluation model. Then, the evaluation value estimation unit 130 estimates the evaluation value corresponding to the combination of the shape information, the surface roughness information, and the material information.

[0057] In step S113, the output unit 400 outputs the evaluation value estimated by the evaluation value estimation unit 130.

[0058] [Effects of the Embodiment] As described in detail above, the painting evaluation apparatus, the painting evaluation method, and the painting evaluation program according to the present embodiment acquire shape information representing the curved shape of the painted surface and surface roughness information representing the surface roughness of the painted surface, and use an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including the shape information and the surface roughness information to estimate the evaluation value corresponding to the combination of the shape information and the surface roughness information.

[0059] Thereby, the distinctness of image of the painted surface can be accurately evaluated. In particular, even when the painted surface is a curved surface, the distinctness of image of the painted surface can be accurately evaluated. Further, since the evaluation value is estimated by the evaluation model, dedicated equipment and skilled personnel for evaluating the distinctness of image of the painted surface are not required, and the cost for evaluating the distinctness of image of the painted surface can be reduced.

[0060] Also, in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment, the evaluation model may be a learning model generated by machine learning based on teacher data that includes, as a set, the shape information of the evaluated painted surface, the surface roughness information of the evaluated painted surface, and the evaluation value of the distinctness of image of the evaluated painted surface. Thereby, the distinctness of image of the painted surface can be accurately evaluated by the evaluation model generated from the teacher data regarding the evaluated painted surfaces having various curved surface shapes.

[0061] Further, the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment may acquire the design data of the painted surface as the shape information, or may acquire the measurement data obtained by measuring the painted surface as the shape information. Thereby, the distinctness of image of the painted surface can be accurately evaluated in consideration of the curved shape of the painted surface.

[0062] Furthermore, the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment may acquire the captured image of the painted surface, associate the captured image and the shape information with the surface roughness information, and record the position on the painted surface where the surface roughness information is acquired. Thereby, the position on the painted surface when the surface roughness information is acquired is specified. Also, the distinctness of image of the painted surface can be accurately evaluated.

[0063] Also, the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment may acquire the material information representing the material of the painted surface, and use an evaluation model that outputs the evaluation value of the distinctness of image of the painted surface for an input including the material information, the shape information, and the surface roughness information, to estimate the evaluation value corresponding to the combination of the shape information, the surface roughness information, and the material information. By using, in addition to the shape information and the surface roughness information of the painted surface, the material information of the painted surface for the evaluation of the distinctness of image of the painted surface, the distinctness of image of the painted surface can be accurately evaluated.

[0064] Furthermore, in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to this embodiment, the evaluation model may be a learning model generated by machine learning based on teacher data that includes material information of the evaluated painted surface, shape information of the evaluated painted surface, surface roughness information of the evaluated painted surface, and an evaluation value of the distinctness of image of the evaluated painted surface as a set. Thereby, the distinctness of image of the painted surface can be accurately evaluated by the evaluation model generated from the teacher data related to the evaluated painted surfaces having various curved surface shapes and various materials.

[0065] In addition, the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to this embodiment acquire surface roughness information representing the surface roughness of the painted surface and material information representing the material of the painted surface, and estimate an evaluation value corresponding to the combination of the surface roughness information and the material information using an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including the surface roughness information and the material information.

[0066] Thereby, the distinctness of image of the painted surface can be accurately evaluated. In addition, since the evaluation value is estimated by the evaluation model, dedicated equipment or skilled personnel for evaluating the distinctness of image of the painted surface are not required, and the cost for evaluating the distinctness of image of the painted surface can be reduced.

[0067] Furthermore, in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to this embodiment, the evaluation model may be a learning model generated by machine learning based on teacher data that includes surface roughness information of the evaluated painted surface, material information of the evaluated painted surface, and an evaluation value of the distinctness of image of the evaluated painted surface as a set. Thereby, the distinctness of image of the painted surface can be accurately evaluated by the evaluation model generated from the teacher data related to the evaluated painted surfaces having various surface roughnesses and material aspects.

[0068] In addition, the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to this embodiment may acquire design data related to at least any one of the painting thickness, material reflectance, lightness, chroma, and hue of the painted surface as the material information. Thereby, the distinctness of image of the painted surface can be accurately evaluated while considering the material of the painted surface in more detail.

[0069] Furthermore, the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment may acquire, as material information, measurement data regarding at least any one of the painting thickness, material reflectance, lightness, chroma, and hue of the painted surface obtained by measuring the painted surface. Thereby, it is possible to accurately evaluate the distinctness of image of the painted surface in more detail considering the material of the painted surface.

[0070] Furthermore, the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment may acquire a captured image of the painted surface and record, in association with the captured image, the position on the painted surface where surface roughness information and material information are acquired. Thereby, the position on the painted surface when the surface roughness information and the material information are acquired is specified. Also, it is possible to accurately evaluate the distinctness of image of the painted surface.

[0071] Furthermore, in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment, the evaluation value of the distinctness of image of the painted surface may be an index determined by at least one of the smoothness of the painted surface, the ratio of diffuse reflection in the reflected light on the painted surface, and the resolution of the image reflected on the painted surface. In this way, the criteria for evaluating the distinctness of image of the painted surface are clarified.

[0072] Also, the evaluation model used in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment may be configured by a neural network including an input layer and an output layer. Here, shape information representing the curved shape of the painted surface and surface roughness information representing the surface roughness of the painted surface are included, and the input data input to the input layer and the output data including the evaluation value of the distinctness of image of the painted surface output from the output layer may be associated with each other to train the evaluation model.

[0073] Thereby, the relationship established between the shape information and surface roughness information of the painted surface and the evaluation value of the distinctness of image of the painted surface can be expressed. As a result, it is not necessary to use dedicated equipment or skilled personnel for evaluating the distinctness of image of the painted surface, and the cost for evaluating the distinctness of image of the painted surface can be reduced.

[0074] Furthermore, an evaluation model used in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment may be generated by acquiring teacher data that is a set of shape information representing the curved shape of the painted surface, surface roughness information representing the surface roughness of the painted surface, and an evaluation value of the distinctness of image of the painted surface, and performing machine learning based on the teacher data so as to output an evaluation value of the distinctness of image of the painted surface for an input including the shape information and the surface roughness information. Thereby, an evaluation model can be obtained that represents the relationship established between the shape information and the surface roughness information of the painted surface and the evaluation value of the distinctness of image of the painted surface.

[0075] Also, the evaluation model used in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment may be configured by a neural network including an input layer and an output layer. Here, the input data including the surface roughness information representing the surface roughness of the painted surface and the material information representing the material of the painted surface, and the output data including the evaluation value of the distinctness of image of the painted surface and output from the output layer may be associated with each other to learn the evaluation model.

[0076] Thereby, the relationship established between the surface roughness information and the material information of the painted surface and the evaluation value of the distinctness of image of the painted surface can be expressed. As a result, dedicated equipment or skilled personnel for evaluating the distinctness of image of the painted surface are not required, and the cost for evaluating the distinctness of image of the painted surface can be reduced.

[0077] Furthermore, an evaluation model used in the painting evaluation apparatus, painting evaluation method, and painting evaluation program according to the present embodiment may be generated by acquiring teacher data that is a set of surface roughness information representing the surface roughness of the painted surface, material information representing the material of the painted surface, and an evaluation value of the distinctness of image of the painted surface, and performing machine learning based on the teacher data so as to output an evaluation value of the distinctness of image of the painted surface for an input including the surface roughness information and the material information. Thereby, an evaluation model can be obtained that represents the relationship established between the surface roughness information and the material information of the painted surface and the evaluation value of the distinctness of image of the painted surface.

[0078] Each function shown in the above embodiments can be implemented by one or more processing circuits. The processing circuits include a programmed processor, an electric circuit, etc., and further include devices such as application-specific integrated circuits (ASICs) and circuit components arranged to execute the described functions.

[0079] As described above, the content of the present invention has been described along with the embodiments. However, it is obvious to those skilled in the art that the present invention is not limited to these descriptions, and various modifications and improvements are possible. It should not be understood that the discussions and drawings forming part of this disclosure limit the present invention. Various alternative embodiments, examples, and operation techniques will be apparent to those skilled in the art from this disclosure.

[0080] The present invention naturally includes various embodiments and the like not described herein. Therefore, the technical scope of the present invention is defined only by the invention-specific matters according to the reasonable claims based on the above description.

[0081] This application claims priority based on International Application PCT / IB2021 / 000416 filed on June 21, 2021, and the entire content of this application is incorporated herein by reference.

Description of Reference Numerals

[0082] 11 Shape acquisition unit 13 Material acquisition unit 17 Surface roughness acquisition unit 21 Image acquisition unit 100 Controller 120 Evaluation model setting unit 130 Evaluation value estimation unit 140 Position identification unit 400 Output unit

Claims

1. A surface roughness acquisition unit that acquires surface roughness information representing the surface roughness of a painted surface, A material acquisition unit that acquires material information including information representing at least any one of the type of material constituting the painted surface, the coating thickness of the painted surface, the amount, shape, and orientation of the brightening material included in the coating of the painted surface, A coating evaluation apparatus comprising a controller, wherein The controller Estimates an evaluation value corresponding to the combination of the surface roughness information and the material information by using an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including the surface roughness information and the material information A coating evaluation apparatus characterized by the above.

2. The coating evaluation apparatus according to Claim 1, wherein The evaluation model Is a learning model generated by machine learning based on teacher data that combines the surface roughness information of the evaluated painted surface, the material information of the evaluated painted surface, and the evaluation value of the distinctness of image of the evaluated painted surface A coating evaluation apparatus characterized by the above.

3. The coating evaluation apparatus according to Claim 1, wherein The material information further includes design data regarding at least any one of the material reflectance, lightness, chroma, and hue of the painted surface A coating evaluation apparatus characterized by the above.

4. The coating evaluation apparatus according to Claim 1, wherein The material information further includes measurement data regarding at least any one of the material reflectance, lightness, chroma, and hue of the painted surface, obtained by measuring the painted surface A coating evaluation apparatus characterized by the above.

5. The coating evaluation apparatus according to Claim 1, further comprising An image acquisition unit that acquires a captured image of the painted surface, and wherein The controller Records the position on the painted surface where the surface roughness information and the material information were acquired, in association with the captured image A coating evaluation apparatus characterized by the above.

6. Acquire surface roughness information representing the surface roughness of a painted surface, Acquire material information including information representing at least any one of the type of material constituting the painted surface, the coating thickness of the painted surface, the amount, shape, and orientation of the brightening material included in the coating of the painted surface, Estimate an evaluation value corresponding to the combination of the surface roughness information and the material information by using an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including the surface roughness information and the material information A coating evaluation method characterized by the above.

7. A surface roughness acquisition unit that acquires surface roughness information representing the surface roughness of a painted surface, A material acquisition unit that acquires material information including information representing at least any one of the type of material constituting the painted surface, the painting thickness of the painted surface, the amount, shape, and orientation of the brightening material included in the painting of the painted surface; to a computer for controlling; a step of acquiring the surface roughness information using the surface roughness acquisition unit; a step of acquiring the material information using the material acquisition unit; a step of estimating an evaluation value corresponding to the combination of the surface roughness information and the material information using an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including the surface roughness information and the material information; A painting evaluation program for causing the above to be executed.

8. An evaluation model constituted by a neural network including an input layer and an output layer, surface roughness information representing the surface roughness of the painted surface, and material information including information representing at least any one of the type of material constituting the painted surface, the painting thickness of the painted surface, the amount, shape, and orientation of the brightening material included in the painting of the painted surface including; input data input to the input layer, output data including an evaluation value of the distinctness of image of the painted surface and output from the output layer, being learned in association with each other An evaluation model characterized by the above.

9. Surface roughness information representing the surface roughness of the painted surface, material information including information representing at least any one of the type of material constituting the painted surface, the painting thickness of the painted surface, the amount, shape, and orientation of the brightening material included in the painting of the painted surface, an evaluation value of the distinctness of image of the painted surface acquiring teacher data in a set, performing machine learning based on the teacher data, and generating an evaluation model that outputs an evaluation value of the distinctness of image of the painted surface for an input including the surface roughness information and the material information An evaluation model generation method characterized by the above.

Images