Image inspection device, image inspection method and image inspection program

Abstract

To provide an image inspection device, an image inspection method and an image inspection program which are high in accuracy of determining pass / fail.SOLUTION: An image inspection device comprises: a learning dataset acquisition unit which acquires a learning dataset; a learning feature amount deriving unit which derives a learning feature amount of learning image data; a clustering processing unit which, based on the learning feature amount, clusters the learning image data to divide the data into a plurality of clusters; an index deriving unit which, for each cluster, derives gravity centers and distances of a plurality of learning feature amounts belonging to the cluster, and derives an average μ and a variance σ of a plurality of distances corresponding to the plurality of learning feature amounts; a captured feature amount deriving unit which extracts a captured feature amount based on captured data; a minimum distance deriving unit which derives each distance between the captured feature amount and the plurality of gravity centers, and derives a minimum distance X which is the smallest among the distances; an abnormality degree calculation unit which calculates an abnormality degree α based on the minimum distance X, the average μ, and the variance σ; and a comparison unit which compares the abnormality degree α with a preset threshold value.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an image inspection device, an image inspection method, and an image inspection program. [Background technology]

[0002] In the past, when inspecting products using image processing, there was an image inspection method that combined multiple judgment algorithms to make a final pass / fail judgment on the product in order to prevent defective products from being mistakenly judged as good products (for example, Patent Document 1).

[0003] However, with conventional image inspection methods, the judgment scores can be unstable and it is not possible to clearly distinguish between good and bad products using a specific threshold. Therefore, there is room for further improvement in the accuracy of the final pass / fail judgment of products. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 6999150 Summary of the Invention [Problem to be solved by the invention]

[0005] An object of the present invention is to provide an image inspection device, an image inspection method, and an image inspection program that have high accuracy in pass / fail judgment. [Means for solving the problem]

[0006] (1) An image inspection device according to one aspect of the present invention includes a training dataset acquisition unit that acquires a training dataset including a plurality of training image data; a training feature derivation unit that derives training features of the training image data; a clustering processing unit that clusters the training image data based on the training features to divide the training image data into a plurality of clusters; an index derivation unit that derives, for each cluster, the center of gravity of the plurality of training features belonging to the cluster and the distance between the center of gravity and the training feature, and derives an average μ and a variance σ of the plurality of distances corresponding to the plurality of training features; an imaging feature derivation unit that extracts imaging features based on imaging data captured by a camera; a minimum distance derivation unit that derives each distance between the imaging feature and the plurality of centers of gravity and derives a minimum distance X that is the smallest of the distances; an abnormality degree calculation unit that calculates an abnormality degree α based on the minimum distance X, the average μ, and the variance σ; and a comparison unit that compares the abnormality degree α with a predetermined threshold. (2) In the above (1), an error processing unit may be provided that executes error processing when the abnormality degree α exceeds the threshold value. (3) In the above (1) or (2), the clustering processing unit may perform the clustering multiple times using random numbers, and the index derivation unit may derive the center of gravity for each cluster each time the clustering is performed, and may use a representative cluster that has a minimum residual sum of squares of the distances in the clusters as a population for calculating the minimum distance X. (4) In the above (1) or (2), the feature amount deriving unit may perform resizing, window function processing, FFT processing, or band-pass filter processing. (5) An image inspection method according to one aspect of the present invention includes: a training dataset acquisition step of acquiring a training dataset including a plurality of training image data; a training feature derivation step of deriving training features of the training image data; a clustering processing step of clustering the training image data based on the training features to divide the training image data into a plurality of clusters; an index derivation step of deriving, for each cluster, the center of gravity of the plurality of training features belonging to the cluster and the distance between the center of gravity and the training features, and deriving an average μ and a variance σ of the plurality of distances corresponding to the plurality of training features; an imaging feature derivation step of extracting imaging features based on imaging data captured by a camera; a minimum distance derivation step of deriving each distance between the imaging features and the plurality of centers of gravity and deriving a minimum distance X that is the smallest of the distances; an abnormality degree calculation step of calculating an abnormality degree α based on the minimum distance X, the average μ, and the variance σ; and a comparison step of comparing the abnormality degree α with a predetermined threshold. (6) An image inspection program according to one aspect of the present invention causes a computer to execute: a training dataset acquisition function for acquiring a training dataset including a plurality of pieces of training image data; a training feature derivation function for deriving training features of the training image data; a clustering processing function for clustering the training image data based on the training features to divide the training image data into a plurality of clusters; an index derivation function for deriving, for each cluster, the center of gravity of the plurality of training features belonging to the cluster and the distance between the center of gravity and the training features, and deriving the average μ and variance σ of the plurality of distances corresponding to the plurality of training features; an imaging feature derivation function for extracting imaging features based on imaging data captured by a camera; a minimum distance derivation function for deriving each distance between the imaging features and the plurality of centers of gravity and deriving a minimum distance X that is the smallest of the distances; an abnormality degree calculation function for calculating an abnormality degree α based on the minimum distance X, the average μ, and the variance σ; and a comparison function for comparing the abnormality degree α with a predetermined threshold. [Effects of the Invention]

[0007] According to the present invention, it is possible to provide an image inspection device, an image inspection method, and an image inspection program that have high accuracy in determining whether an object is pass or fail. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an overview of an image inspection device. [Figure 2] FIG. 1 is a diagram illustrating an outline of an image inspection method. [Figure 3] FIG. 1 is a diagram illustrating a flow of an image inspection method. [Figure 4] FIG. 10 is a diagram illustrating the flow of a feature derivation step. DETAILED DESCRIPTION OF THE INVENTION

[0009] (Embodiment) Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Fig. 1 is a diagram illustrating an overview of an image inspection device 100. Fig. 2 is a diagram illustrating an overview of an image inspection method. Note that, hereinafter, parts having common functions may be given the same reference numerals or symbols.

[0010] (Image inspection equipment) The image inspection device 100 according to the embodiment is used to inspect the appearance of products such as electronic components, for example.

[0011] 1, the image inspection device 100 includes a control means S. The image inspection device 100 may include a camera C.

[0012] The camera C appropriately captures an image of the product (component) to be inspected. The captured image data (image data) is sent to the control means S. Note that image data captured by an external camera not included in the image inspection device 100 may be input to the control means S of the image inspection device 100, allowing the image inspection device 100 to subsequently perform an inspection on the captured image data.

[0013] The control means S is a computer system including a CPU, a memory M (storage medium) such as a RAM, a ROM, and an auxiliary storage device, an external connection interface IF with an input / output device, and a bus B. The control means S may include a network interface. The memory M stores an image inspection program for causing the control means S (computer system) to execute the image inspection method according to this embodiment.

[0014] The CPU is an arithmetic circuit that controls the control means S. The CPU reads out programs stored in the ROM or auxiliary storage device into the RAM. The CPU executes various processes in the programs read out into the RAM. The ROM stores system programs and the like used to control the control means S. The auxiliary storage device stores application programs and the like that execute various processes. The auxiliary storage device is, for example, an HDD or SSD. The external connection interface IF is an interface for connecting various devices to the control means S. The external connection interface IF connects, for example, a camera C, a display D, a keyboard K, etc. to the control means S. The network interface functions to communicate via a network based on the control of the CPU. The bus B communicatively connects the above-mentioned functional units that make up the control means S.

[0015] The image inspection device 100 takes an image of the product to be inspected using a camera C, and based on the captured image data, a control means S determines whether the product to be inspected is pass / fail (whether the product is good or defective, normal or abnormal).

[0016] As shown in FIG. 2, the image inspection device 100 performs inspection based on image data obtained by capturing an image of a product. The image inspection device 100 performs a parallel combination of defective product judgment, which judges whether the image data to be inspected is a good product or a defective product, and defective product judgment, which judges whether the image data is normal or abnormal. The image inspection device 100 then outputs an inspection result in which the judgment result is OK (pass) if the product is good and normal, or NG (pass) if the product is defective or abnormal.

[0017] Defective product judgment is based on pass / fail rules learned from images of good and defective products collected in advance. When an unknown defective product image is input as an image to be inspected during the learning process, there is a possibility that an image that should actually be judged as defective will be mistakenly judged as good.

[0018] In order to prevent erroneous judgments in defective product judgments, it is preferable that defective product judgments be performed in parallel in combination with defective product judgments. The defective product judgment includes a process of learning the likelihood of a product being good as a feature from only good product images, and then calculating a defective product judgment score for the image data of the inspection target. The defective product judgment is made based on whether the defective product judgment score exceeds a threshold. The defective product determination score is normalized to a certain range. Specific methods for calculating the defective product determination score include, for example, an algorithm using an autoencoder or an algorithm combining kernel density estimation and a projected histogram.

[0019] In this way, the image inspection device 100 performs defective product judgment and abnormal product judgment in parallel, and by combining these, it is possible to prevent erroneous judgment due to defective product judgment by abnormal product judgment, and to perform highly accurate inspection. Note that the image inspection device 100 may perform only abnormal product judgment.

[0020] However, algorithms using autoencoders may not produce stable anomaly detection scores for normal images. Furthermore, when the image data to be inspected is a color image, the anomaly detection scores may not be clearly separated between normal and abnormal images, resulting in inaccurate anomaly detection. Furthermore, when using a method that combines kernel density estimation and projected histograms, the anomaly detection scores must be properly normalized.

[0021] Therefore, the image inspection device 100 includes a training dataset acquisition unit 10 that acquires a training dataset including multiple training image data; a training feature derivation unit 20 that derives training features of the training image data; a clustering processing unit 30 that performs clustering of the training image data based on the training features; an index derivation unit 40 that derives, for each cluster obtained by clustering, the center of gravity of the multiple training features belonging to the cluster and the distance between the center of gravity and the training feature, and derives the average μ and variance σ of the multiple distances corresponding to the multiple training features; an imaging feature derivation unit 50 that extracts imaging features based on imaging data captured by camera C; a minimum distance derivation unit 60 that derives each distance between the imaging features and the multiple centers of gravity and derives a minimum distance X that is the smallest of the distances; an abnormality degree calculation unit 70 that calculates an abnormality degree α based on the minimum distance X, the average μ, and the variance σ; and a comparison unit 80 that compares the abnormality degree α with a preset threshold. This allows the system to calculate the abnormality degree α, which is an appropriately normalized evaluation score, even when the product to be inspected has multiple variations or the degree of variability in pass / fail products varies depending on the type of product, making it possible to determine whether the product to be inspected is pass / fail based on a stable evaluation score with a clear threshold between pass / fail products.As a result, the system can provide an image inspection device 100 with high pass / fail determination accuracy.

[0022] The above-mentioned components of the image inspection device 100 can be referred to as modules or units.

[0023] The abnormality level α is calculated using the following formula: α={(X-μ) 2 / σ}. This allows the degree of anomaly α to be calculated as an appropriately normalized evaluation score, taking into account the minimum distance X, mean μ, and variance σ. This allows the degree of anomaly α to be calculated, which is an appropriately normalized evaluation score, and absorbs the fact that the degree of variation in the appearance of the product being inspected differs depending on the type of product, while also allowing the degree of anomaly α seen by the user to always be on the same scale.

[0024] The image inspection device 100 may include an error processing unit 90 that executes error processing when the abnormality degree α exceeds a threshold value. The error processing may involve, for example, displaying on a display that the inspection result of the inspected product is NG (abnormal or defective). This notifies the user that the product inspection result is NG.

[0025] The clustering processing unit 30 may perform clustering multiple times using random numbers. Then, the index derivation unit 40 may derive a center of gravity for each cluster each time clustering is performed multiple times, and use the representative cluster with the smallest residual sum of squares of distances in the cluster as the population for calculating the minimum distance X. This makes it possible to derive a center of gravity that best captures the characteristics of the training dataset, even if the results change with each clustering attempt, thereby providing an image inspection device 100 with higher pass / fail judgment accuracy.

[0026] (Image inspection method) Next, the flow of the image inspection method will be described. The image inspection method can be performed using image inspection device 100. Each step in the image inspection method can be performed by a corresponding unit in image inspection device 100. Figure 3 is a diagram illustrating the flow of the image inspection method.

[0027] (1) As shown in FIG. 3, first, a training dataset including a plurality of training image data is acquired (training dataset acquisition step). The training image data are images of non-defective products. In other words, the training dataset may be composed only of non-defective product images. The training dataset acquisition step can be executed by the training dataset acquisition unit 10 in the image inspection device 100.

[0028] (2) Next, learning features of the training image data acquired in the training dataset acquisition step (process) are derived (learning feature derivation step). The learning features may be derived using an appropriate algorithm based on the training image data. The algorithm for deriving the learning features may be, for example, a method that uses a set of brightness differences in an image as features (Haar-Like), a method that uses features focusing on the distribution of brightness gradient directions in a grayscale image (HOG), a method that extracts features in two stages: feature point detection and feature description (SIFT), a filter method, a wrapper method, etc. The learning feature derivation step can be executed by the learning feature derivation unit 20 in the image inspection device 100.

[0029] Here, the learning feature derivation step may be executed according to the following flow. Note that the imaging feature derivation step described later may also be executed according to a flow similar to the flow of the learning feature derivation step shown here. Fig. 4 is a diagram illustrating the flow of the feature derivation step.

[0030] (2-1) As shown in Fig. 4, image data (learning image data or captured image data) may be resized. The resizing process is an operation to change the size of image data in order to unify the input size of a machine learning model.

[0031] (2-2) Next, the image data (learning image data or captured image data) that has been appropriately resized may be padded. Padding is a process in which meaningless data is added before or after short data to adjust its length so that the data can be treated as a fixed length.

[0032] (2-3) Next, window function processing may be performed on the image data (learning image data or captured image data) that has been appropriately padded. Window function processing is a process of multiplying image data by a window function in order to extract only information within a finite interval.

[0033] (2-4) Next, the image data (learning image data or captured image data) that has been appropriately window function processed may be subjected to FFT processing. The FFT processing is a process of Fourier transforming the image data to convert the spatial domain into the spatial frequency domain based on the image data and obtain the amplitude spectrum distribution.

[0034] (2-5) Next, band-pass filtering may be performed on the image data (learning image data or captured image data) that has been subjected to the FFT processing as appropriate. Band-pass filtering is a process that compresses image data by passing only frequency components within a specific range, thereby removing components that have little effect on the judgment.

[0035] (3) Returning to FIG. 3 , next, based on the learning features derived in the learning feature derivation step, the learning image data is clustered and divided into multiple clusters (clustering processing step). Clustering is a machine learning technique that groups data based on the similarity between data. Clustering may be hierarchical clustering using algorithms such as shortest distance, longest distance, average distance, center of gravity, or Ward's method, or non-hierarchical clustering such as k-means. The algorithm used for clustering may be selected using random numbers each time clustering processing is performed. The algorithm used for clustering may also be changed using random numbers each time clustering processing is performed. Specifically, for example, one algorithm may be defined, and the random numbers used in it may be reassigned to perform clustering multiple times. The clustering processing step can be performed by the clustering processing unit 30 in the image inspection device 100.

[0036] (4) Next, for each cluster obtained in the clustering process, the center of gravity of the multiple learning features belonging to the cluster and the distance between the center of gravity and each learning feature are derived. Then, the mean μ and variance σ of the multiple distances derived for the multiple learning features are derived (index derivation step). The index derivation step can be performed by the index derivation unit 40 in the image inspection device 100. Through the above steps, a standard index of a non-defective product can be derived as a pre-processing step for the inspection.

[0037] (5) Next, imaging features are extracted based on the imaging data captured by the camera (imaging feature derivation step). The imaging data is image data obtained by capturing an image of the product to be inspected with a camera. The imaging features may be derived based on the imaging data using an appropriate algorithm. The extraction of the imaging features may be the same method as that used to derive the learning features in the learning feature derivation step. The imaging feature derivation step can be executed by the imaging feature derivation unit 50 in the image inspection device 100.

[0038] (6) Next, the distances between the imaging feature and the multiple centers of gravity are derived, and the minimum distance X, which is the smallest among the distances, is derived (minimum distance derivation step). The minimum distance derivation step can be executed by minimum distance derivation unit 60 in image inspection device 100. (7) Next, based on the minimum distance X derived in the minimum distance derivation step, and the mean μ and variance σ derived in the index derivation step, specifically, the calculation formula (α={(X-μ) 2 / σ}) (abnormality degree calculation step). The abnormality degree calculation step can be executed by the abnormality degree calculation unit 70 in the image inspection device 100.

[0039] (8) Next, the degree of abnormality α calculated in the degree of abnormality calculation step is compared with a preset threshold (comparison step). The comparison step can be executed by the comparison unit 80 in the image inspection device 100.

[0040] As explained above, the image inspection method includes the steps described above. This makes it possible to stabilize the judgment score and clearly distinguish between good and bad products using a specific threshold. This improves the accuracy of the final pass / fail judgment of the product.

[0041] (9) Furthermore, if necessary, after the comparison step, if the degree of abnormality α exceeds a threshold value, error processing may be executed (error processing step). The error processing may, for example, be to display on the display D a message that the inspection result of the inspected product is NG (abnormal or defective). This notifies the user that the product inspection result is NG. The error processing step may be executed by the error processing unit 90 in the image inspection device 100.

[0042] (Image inspection program) The image inspection program has the function of causing a computer to execute each process performed at each step in the image inspection method described above. Specifically, the image inspection program has a learning dataset acquisition function, a learning feature derivation function, a clustering processing function, an index derivation function, an imaging feature derivation function, a minimum distance derivation function, an anomaly degree calculation function, and a comparison function, corresponding to each step in the image inspection method described above. This makes it possible to stabilize the judgment score and clearly distinguish between good and bad products using a specific threshold. This improves the accuracy of the final pass / fail judgment of the product.

[0043] The technical scope of the present invention is not limited to the above-described embodiments, and various modifications can be made without departing from the spirit of the present invention.

[0044] In addition, the components in the above-described embodiments may be replaced with known components as appropriate without departing from the spirit of the present invention. Furthermore, the above-described modifications may be combined as appropriate without departing from the spirit of the present invention.

[0045] As described above, the image inspection device 100 according to the embodiment includes a training dataset acquisition unit 10 that acquires a training dataset including multiple pieces of training image data; a training feature derivation unit 20 that derives training features of the training image data; a clustering processing unit 30 that performs clustering of the training image data based on the training features; an index derivation unit 40 that, for each cluster obtained by clustering, derives the center of gravity of the multiple training features belonging to the cluster and the distance between the center of gravity and the training features, and derives the average μ and variance σ of the multiple distances corresponding to the multiple training features; an imaging feature derivation unit 50 that extracts imaging features based on imaging data captured by a camera C; a minimum distance derivation unit 60 that derives each distance between the imaging features and the multiple centers of gravity and derives the minimum distance X that is the smallest of the distances; an abnormality degree calculation unit 70 that calculates an abnormality degree α based on the minimum distance X, the average μ, and the variance σ; and a comparison unit 80 that compares the abnormality degree α with a preset threshold. This allows the calculation of the abnormality degree α, which is an appropriately normalized evaluation score, so that pass / fail judgment of the product to be inspected can be performed based on a stable evaluation score with a clear threshold for determining whether a product is good or bad. As a result, an image inspection device 100 with high pass / fail judgment accuracy can be provided.

[0046] The image inspection method according to the embodiment includes a training dataset acquisition step of acquiring a training dataset including multiple training image data, a training feature derivation step of deriving training features from the training image data, a clustering processing step of clustering the training image data based on the training features to divide the training image data into multiple clusters, an index derivation step of deriving, for each cluster, the center of gravity of the multiple training features belonging to the cluster and the distances between the center of gravity and the training features, and deriving the mean μ and variance σ of the multiple distances corresponding to the multiple training features, an imaging feature derivation step of extracting imaging features based on imaging data captured by a camera, a minimum distance derivation step of deriving each distance between the imaging features and the multiple centers of gravity and deriving the smallest distance X among the distances, an anomaly degree calculation step of calculating an anomaly degree α based on the minimum distance X, the mean μ, and the variance σ, and a comparison step of comparing the anomaly degree α with a predetermined threshold. The anomaly degree α is calculated as an appropriately normalized evaluation score, enabling pass / fail judgment of the product to be inspected based on a stable evaluation score with a clear threshold for distinguishing between good and bad products. Therefore, an image inspection method with high accuracy in pass / fail judgment can be provided.

[0047] According to an embodiment, an image inspection program causes a computer to execute the following functions: a training dataset acquisition function for acquiring a training dataset containing multiple pieces of training image data; a training feature derivation function for deriving training features from the training image data; a clustering processing function for clustering the training image data based on the training features to divide the training image data into multiple clusters; an index derivation function for deriving, for each cluster, the center of gravity of the multiple training features belonging to the cluster and the distances between the center of gravity and the training features, and deriving the mean μ and variance σ of the multiple distances corresponding to the multiple training features; an imaging feature derivation function for extracting imaging features based on imaging data captured by a camera; a minimum distance derivation function for deriving each distance between the imaging features and the multiple centers of gravity and deriving the minimum distance X, which is the smallest of the distances; an anomaly degree calculation function for calculating an anomaly degree α based on the minimum distance X, the mean μ, and the variance σ; and a comparison function for comparing the anomaly degree α with a preset threshold. The anomaly degree α is calculated as an appropriately normalized evaluation score, enabling pass / fail judgment of products to be inspected based on a stable evaluation score with a clear threshold for distinguishing between good and bad products. Therefore, an image inspection program with high pass / fail judgment accuracy can be provided. [Explanation of symbols]

[0048] 100 Image inspection device 10 Learning dataset acquisition section 20 Learning feature extraction unit 30 Clustering processing section 40 Indicator derivation part 50 Image feature extraction unit 60 Minimum distance derivation part 70 Abnormality calculation unit 80 Comparison section 90 Error processing section α Abnormality μ average σ variance B Bus C Camera D Display K keyboard M Memory S Control means X minimum distance

Claims

1. a training dataset acquisition unit that acquires a training dataset including a plurality of training image data; a learning feature derivation unit that derives learning features of the learning image data; a clustering processing unit that clusters the learning image data based on the learning feature amount and divides the learning image data into a plurality of clusters; an index derivation unit that derives, for each cluster, a center of gravity of the plurality of learned features belonging to the cluster and a distance between the center of gravity and the learned feature, and derives a mean μ and a variance σ of the plurality of distances corresponding to the plurality of learned feature; an imaging feature derivation unit that extracts imaging feature amounts based on imaging data captured by a camera; a minimum distance deriving unit that derives distances between the imaging feature amount and the plurality of centers of gravity and derives a minimum distance X that is the smallest of the distances; an abnormality degree calculation unit that calculates an abnormality degree α based on the minimum distance X, the mean μ, and the variance σ; a comparison unit that compares the abnormality degree α with a preset threshold value; An image inspection device comprising:

2. an error processing unit that executes error processing when the abnormality degree α exceeds the threshold value; The image inspection device according to claim 1 .

3. the clustering processing unit performs the clustering multiple times using random numbers; The index derivation unit derives the center of gravity for each cluster each time the clustering is performed, and determines a representative cluster for which the residual sum of squares of the distances in the clusters is smallest as a population for calculating the minimum distance X.

3. The image inspection device according to claim 1 or 2.

4. The feature derivation unit performs resizing, window function processing, FFT processing, or band-pass filter processing.

3. The image inspection device according to claim 1 or 2.

5. a learning dataset acquisition step of acquiring a learning dataset including a plurality of learning image data; a learning feature derivation step of deriving learning features of the learning image data; a clustering processing step of clustering the learning image data into a plurality of clusters based on the learning feature; an index derivation step of deriving, for each cluster, a center of gravity of the plurality of learning features belonging to the cluster and a distance between the center of gravity and the learning feature, and deriving a mean μ and a variance σ of the plurality of distances corresponding to the plurality of learning features; an imaging feature derivation step of extracting imaging feature amounts based on imaging data captured by a camera; a minimum distance deriving step of deriving distances between the imaging feature amount and the plurality of centers of gravity and deriving a minimum distance X that is the smallest of the distances; an abnormality degree calculation step of calculating an abnormality degree α based on the minimum distance X, the mean μ, and the variance σ; a comparison step of comparing the abnormality degree α with a preset threshold value; An imaging inspection method comprising:

6. On the computer, a training dataset acquisition function for acquiring a training dataset including a plurality of training image data; a learning feature derivation function for deriving learning features of the learning image data; a clustering processing function for clustering the learning image data based on the learning feature amount and dividing the learning image data into a plurality of clusters; an index derivation function that derives, for each cluster, a center of gravity of the plurality of learning features belonging to the cluster and a distance between the center of gravity and the learning feature, and derives a mean μ and a variance σ of the plurality of distances corresponding to the plurality of learning features; an imaging feature derivation function for extracting imaging feature amounts based on imaging data captured by a camera; a minimum distance deriving function that derives distances between the imaging feature amount and the plurality of centers of gravity and derives a minimum distance X that is the smallest of the distances; an abnormality degree calculation function that calculates an abnormality degree α based on the minimum distance X, the mean μ, and the variance σ; a comparison function that compares the abnormality degree α with a preset threshold value; An image inspection program that executes the above.

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