Omron Corporation

Masashi Kurita

Yasuyuki Ikeda

Yuki Hanzawa

Sakon Yamamoto

Yosuke Naruse

Yutaka Kato

Yoshihisa Minato

Norikazu Tonogai

Tadashi Hyuga

Shigenori Nagae

Yuki Hasegawa

Masamichi Oe

An inspection apparatus according to one or more embodiments extracts an attention area from a target image using a first estimation model, performs a computational process with a second estimation model using the extracted attention area, and determines whether a target product has a defect based on a computational result from the second estimation model. The first estimation model is generated based on multiple first training images of defect-free products in a target environment. The second estimation model is generated based on multiple second training images of defects. The computational process with the second estimation model includes generating multiple feature maps with different dimensions by projecting the target image into different spaces with lower dimensions. The extracted attention area is integrated into at least one of the multiple feature maps in the computational process with the second estimation model.

Inspection apparatus, unit selection apparatus, inspection method, and computer-readable storage medium storing an inspection program

A defect inspecting device including an acquisition unit, an image generating unit, an inspection unit, and a setting unit. The image generating unit generates one or more feature extraction images by applying to an inspection image a learned classifier. The inspection unit identifies a region corresponding to a defect based on one or more determination parameters and a binarized image generated based on the feature extraction image. The setting unit provisionally sets the determination parameters on premise of a post-adjustment from a user when a number of image data for learning corresponding to the features is less than a threshold value in a case where the region corresponding to the defect is identified based on the pre-learned features, and sets the determination parameters according to designation from the user in a case where the region corresponding to the defect is identified based on a feature other than the pre-learned features.

Defect inspecting device, defect inspecting method, and storage medium

This image processing device, by superimposing a defect imageon a background image, with the original image of an inspection subject as the background image, and performing image processing in which the display format of the defect imageor the display format of the background imageis altered, a plurality of composite imageshaving different ways of seeing the defect imagewith respect to the background imageare generated (step), the advisability of detecting the defect imagefrom each of the plurality of composite imagesis verified (step), the detectable rangeof the defect imageis estimated on the basis of the detection advisability verification results (step), and the detectable rangeis displayed (step).

Image processing device, image processing method, and image processing non-transitory computer readable medium for verifying detectable range of defect image

The present disclosure relates to a method, device, system and computer-program product for setting a lighting condition when an object is checked and a storage medium. The method includes that: the object is lighted by light sources capable of changing lighting parameters, and the object is captured by an image sensor in such lighting parameters to obtain captured images, wherein the object has known label data; and a part of or all of the captured images and the corresponding label data of the object are applied to learning of a machine learning model, and the lighting condition and the check algorithm parameters of the machine learning model is set simultaneously by optimizing both the lighting parameters and the check algorithm parameters, on the basis of a comparison result between an estimation result of the machine learning model and the label data. Therefore, operations are simplified.

Method, device, system and computer-program product for setting lighting condition and storage medium

A convolutional neural network performs: a first masking process of masking a pixel region not to be inspected, by computing pixel values of corresponding pixels of an inspection image and of a mask image; an intermediate process for extracting a feature image from the inspection image that has been subjected to the first masking process; and a second masking process of masking the pixel region not to be inspected, by computing the pixel values of corresponding pixels of the inspection image that has been subjected to the intermediate process and of the mask image that has been subjected to a process identical to the intermediate process.

Image processing device, image processing method, and computer readable recording medium

Provided in the present disclosure are a method, an apparatus and a system for determining feature data of image data, and a storage medium. Wherein, the method comprises: acquiring features of image data, the features comprising a first feature and a second feature, wherein, the first feature is extracted from the image data using a first model, the first model being trained in a machine learning manner, and the second feature is extracted from the image data using a second model, the second model being constructed based on a pre-configured data processing algorithm; and determining feature data based on the first feature and the second feature. The present disclosure solves the technical problem that features recognized by the AI may not be consistent with human recognized features.

Method, apparatus and system for determining feature data of image data, and storage medium

An image generating part generating feature extraction images by applying an identification part, which has completed learning, that has executed learning in advance to extract features using learning image data to an inspection image, an inspection part specifying an area corresponding to a defect on the basis of judgment parameters for judging presence/absence of a defect in the inspection target object and a binary image generated on the basis of the feature extraction images, and a setting part calculating an image score based on a density of a color of pixels of a setting image using the setting image that is the binary image in which an area corresponding to the defect is specified and updating the judgment parameters such that a difference between an image score of the inside of the area and an image score of the outside of the area becomes relatively large are included.

Defect inspection device, defect inspection method, and computer readable recording medium

The present disclosure provides a method, device, system and computer-program product for setting a lighting condition when an object is checked and a storage medium. The method includes that: the object is lighted by light sources capable of changing lighting parameters specifying the lighting condition when the object is captured, and the object with corresponding label data is captured by an image sensor under multiple such lighting parameters to obtain multiple captured images; estimation images of the object are generated on the basis of image data sets obtained by associating the captured images and the corresponding lighting parameters; and the estimation images and the corresponding label data are applied to learning of the machine learning model, and the lighting condition is set on the basis of a comparison result between an estimation result of a machine learning model and the label data. Therefore, operations are simplified.

A data generation apparatus includes: an acquisition unit configured to acquire an image of an object to be inspected including a defect, and a region of the image that includes the defect; a correction unit configured to correct the region acquired by the acquisition unit by expanding an outer edge of the region so that the number of pixels included in the region is increased by a predetermined amount; and a generation unit configured to generate learning data by associating the region corrected by the correction unit with the image.

Data generation apparatus, data generation method, and data generation program

An inspection apparatus includes: an image capturing apparatus configured to capture an image of an object to be inspected: a determination unit configured to determine, based on the image, whether or not the object to be inspected includes a defect, using an identification device that has been trained using learning data: an input unit configured to accept an input indicating whether or not a determination result by the determination unit is correct; an extraction unit configured to extract a partial image of the image based on which the determination has been made; and a generation unit configured to generate new learning data based on the partial image, if a fact that the determination result by the determination unit is not correct has been input.

Inspection apparatus, data generation apparatus, data generation method, and data generation program

A data generation apparatus includes: an acquisition unit configured to acquire a result determined by a determination unit, which uses an identification device trained using learning data, as to whether or not an object to be inspected includes a part to be detected based on an image of the object to be inspected; an evaluation unit configured to evaluate whether or not the determination result is correct; and a generation unit configured to generate, if the evaluation unit has evaluated that the determination result is not correct, new learning data by associating at least one of the image and a composite image generated based on the image with information in which the determination result is corrected.

The disclosure includes: a memory section, storing a learned model and an internal parameter set in the learned model; an acquisition section, acquiring an image of an inspection object photographed under predetermined conditions; a preprocessing section, generating a predetermined preprocessing filter according to a feature value in a preprocessing image being an image of the inspection object that is acquired by the acquisition section and that includes a defect, and a feature value corresponding to the internal parameter, and generating a preprocessed image by applying the generated preprocessing filter to an inspection image being an image of the inspection object that is acquired by the acquisition section and converting the inspection image; and an inspection section, inspecting the preprocessed image for presence or absence of the defect of the inspection object by using the stored learned model.

Defect inspection apparatus, defect inspection method, and non-transitory computer readable medium

An image processing method for identifying a region in an input image by character recognition, the region coinciding with a predetermined search condition, includes receiving the search condition, the search condition including assignments of plural format character strings, each format character string including an assignment of a character type or a specific character for each character of a recognition target, extracting a character string region becoming a candidate from the input image, calculating a similarity between a character recognition result and the plural format character strings with respect to each group of plural character string regions, the character recognition result being of each character string region included in each group, and determining the group coinciding with the search condition among the groups of plural character string regions according to the calculated similarity.

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Kizugawa, Japan

Masashi Kurita