# US 2025/0095129 A1 · Method and device for automatic optical inspection

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- Number: US 2025/0095129 A1
- Document ID: US20250095129A1
- Status: Published application
- Kind: A1
- Application number: 18/826,744
- Filed: 2024-09-06
- Published: 2025-03-20
- Earliest claimed: 2023-09-18
- Assignees: Robert Bosch GmbH
- Applicants: Robert Bosch GmbH
- Inventors: Jiabo He; Yige Li
- Web page: https://patentgrep.com/patent/US20250095129A1

## CPC

- G06T 7/0004: {Industrial image inspection}
- G06V 10/26: Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V 10/761: {Proximity, similarity or dissimilarity measures}
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/7715: {Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods}
- G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
- G06V 10/776: Validation; Performance evaluation
- G06V 10/82: using neural networks
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30164: Workpiece; Machine component
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

A method for automatic optical inspection (AOI) includes (i) obtaining an image of an inspected component of a first domain, (ii) inputting the image of the inspected component into an inspection model, the inspection model comprising a generic feature extraction sub-model and at least one task inspection sub-model associated with a particular AOI task of the first domain, (iii) generating, by the generic feature extraction sub-model, a first feature representation of the image based on the image of the inspected component, and (iv) generating, by the at least one task inspection sub-model, at least one inspection result associated with the particular AOI task of the first domain based on the first feature representation of the image.

## Claims

### Claim 1

A method for automatic optical inspection (AOI), comprising:
obtaining an image of an inspected component of a first domain, the first domain corresponding to a first industrial component type or corresponding to a first industrial component production line;
inputting the image of the inspected component into an inspection model, the inspection model comprising a generic feature extraction sub-model and at least one task inspection sub-model associated with a particular AOI task of the first domain;
generating, by the generic feature extraction sub-model, a first feature representation of the image based on the image of the inspected component; and
generating, by the at least one task inspection sub-model, at least one inspection result associated with the particular AOI task of the first domain based on the first feature representation of the image.

### Claim 2 (depends on 1)

The method of claim 1, wherein the particular AOI task of the first domain comprises at least one or at least two of the following tasks:
a classification task for classifying the image of the inspected component into one class of a plurality of classes; a segmentation task for segmenting a defective area in the image of the inspected component; and an identification task for identifying the defective area in the image of the inspected component.

### Claim 3 (depends on 2)

The method of claim 2, wherein when the particular AOI task of the first domain is the classification task, the task inspection sub-model is a multi-layer perceivorator (MLP) model;
when the particular AOI task of the first domain is the segmentation task, the task inspection sub-model is a mask decoder of a convolutional neural network (CNN) model or a decoder based on an attention mechanism; and
when the particular AOI task of the first domain is the identification task, the task inspection sub-model is a boundary frame decoder with a classification result of the CNN model or the decoder based on the attention mechanism.

### Claim 4 (depends on 1)

The method of claim 1, wherein the generic feature extraction sub-model is a visual base model based on an attention mechanism.

### Claim 5 (depends on 4)

The method of claim 4, wherein the generic feature extraction sub-model is trained based on images of industrial components in a plurality of domains, and wherein the plurality of domains correspond to a plurality of industrial component types, respectively or correspond to a plurality of industrial component production lines, respectively.

### Claim 6 (depends on 5)

The method of claim 5, wherein the first domain is one domain in the plurality of domains, or the first domain is one domain other than the plurality of domains, and wherein the task inspection sub-model is trained based on an image of an industrial component in the first domain.

### Claim 7 (depends on 1)

The method of claim 1, further comprising:
obtaining a second image of a second inspected component of a second domain, the second domain corresponding to a second industrial component type or corresponding to a second industrial component production line;
inputting the second image of the second inspected component into the inspection model, the inspection model further comprising at least one second task inspection sub-model associated with a particular AOI task of the second domain;
generating, by the generic feature extraction sub-model, a first feature representation of the second image based on the second image of the second inspected component; and
generating, by the at least one second task inspection sub-model, at least one inspection result associated with the particular AOI task of the second domain based on the first feature representation of the second image.

### Claim 8

A method of training an inspection model for automatic optical inspection (AOI), comprising:
collecting images of industrial components in a plurality of domains as a multi-domain AOI training dataset, wherein the plurality of domains correspond to a plurality of industrial component types, respectively or correspond to a plurality of industrial component production lines, respectively;
training the generic feature extraction sub-model in the inspection model based on the multi-domain AOI training dataset to obtain a trained generic feature extraction sub-model;
training at least one task inspection sub-model associated with a particular AOI task of a first domain in the inspection model based on an image of an industrial component in the first domain as a single-domain AOI training dataset, the first domain corresponding to a first industrial component type or corresponding to a first industrial component production line, wherein the trained generic feature extraction sub-model receives an image in the single-domain AOI training dataset and outputs a first feature representation of the image, and the at least one task inspection sub-model receives the first feature representation of the image and predicts at least one inspection result associated with the particular AOI task of the first domain.

### Claim 9 (depends on 8)

The method of claim 8, wherein training the generic feature extraction sub-model in the inspection model based on the multi-domain AOI training dataset comprises:
performing data enhancement on the multi-domain AOI training dataset without a label to obtain a multi-domain AOI training image pair set, and training the generic feature extraction sub-model using a comparative learning method based on the multi-domain AOI training image pair set; and/or
training the generic feature extraction sub-model using a masked autoencoder (MAE) method based on the multi-domain AOI training dataset without the label.

### Claim 10 (depends on 9)

The method of claim 9, wherein:
training the generic feature extraction sub-model using the comparative learning method based on the multi-domain AOI training image pair set comprises: receiving, by the generic feature extraction sub-model, an image pair in the multi-domain AOI training image pair set and outputting a first feature representation of the image pair, obtaining a similarity of the image pair by a comparative learning module based on the first feature representation of the image pair, obtaining a comparative learning loss based on the similarity of the image pair, and updating the generic feature extraction sub-model based on the comparative learning loss; and/or
training the generic feature extraction sub-model using the MAE method comprises: masking an original image in the multi-domain AOI training dataset to obtain a masked image, receiving, by the generic feature extraction sub-model, the masked image and outputting a first feature representation of the masked image, predicting, by a decoder module, a restored image based on the first feature representation of the masked image, obtaining an MAE loss based on the restored image and the original image, and updating the generic feature extraction sub-model based on the MAE loss.

### Claim 11 (depends on 8)

The method of claim 8, wherein training at least one task inspection sub-model associated with the particular AOI task of the first domain in the inspection model based on the image of the industrial component in the first domain as the single-domain AOI training dataset comprises: training the task inspection sub-model based on the single-domain AOI training dataset with a label.

### Claim 12 (depends on 8)

The method of claim 8, further comprising:
training at least one second task inspection sub-model associated with a second particular AOI task of a second domain in the inspection model based on an image of an industrial component in the second region as a second single-domain AOI training dataset, the second domain corresponding to a second industrial component type or corresponding to a second industrial component production line, wherein the trained generic feature extraction sub-model receives a second image in the second single-domain AOI training dataset and outputs a first feature representation of the second image, and the at least one second task inspection sub-model receives the first feature representation of the second image and predicts at least one inspection result associated with the second particular AOI task of the second domain.

### Claim 13

A device for automatic optical inspection (AOI), comprising:
an image obtaining module configured to obtain an image of an inspected component of a first domain, the first domain corresponding to a first industrial component type or corresponding to a first industrial component production line;
an inspection module, the inspection module comprising a generic feature extraction sub-module and at least one task inspection sub-module associated with a particular AOI task of the first domain, the generic feature extraction sub-module configured to generate a first feature representation of the image based on the image of the inspected component, and the at least one task inspection sub-module configured to generate at least one inspection result associated with the particular AOI task of the first domain based on the first feature representation of the image.

### Claim 14 (depends on 13)

The device of claim 13, wherein the image obtaining module is configured to obtain a second image of a second inspected component of a second domain, the second domain corresponding to a second industrial component type or corresponding to a second industrial component production line,
wherein the inspection module further comprises at least one second task inspection sub-module associated with a particular AOI task of the second domain, wherein the generic feature extraction sub-module is configured to generate a first feature representation of the second image based on the second image of the second inspected component, and the at least one second task inspection sub-module is configured to generate at least one inspection result associated with the particular AOI task of the second domain based on the first feature representation of the second image.

### Claim 15 (depends on 1)

A system for automatic optical inspection (AOI), comprising:
an image capture apparatus configured to capture an image of an inspected component;
one or more processors; and
one or more memories, the memories having computer-executable instructions stored thereon, and the instructions, when run by the one or more processors, perform the operations of claim 1.

### Claim 16 (depends on 1)

A computer system, comprising:
one or more processors; and
one or more memories, the memories having computer-executable instructions stored thereon, and the instructions, when run by the one or more processors, perform the operations of claim 1.

### Claim 17 (depends on 1)

A machine-readable storage medium having executable instructions stored thereon, the instructions, when executed, cause one or more processors to perform the method of claim 1.