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公开(公告)号：US20220309639A1
公开(公告)日：2022-09-29
申请号：US17309309
申请日：2020-09-10
Applicant: GOERTEK INC.
Inventor： Jie LIU
IPC: G06T7/00 , G06V10/94 , G06V10/764 , G06V10/774 , G06V10/776 , G06V10/54 , G06T7/529 , G06T7/73 , G06V10/22 , G06T7/155 , G06V10/34 , G06T7/11 , G06T7/60 , G06T7/13
Abstract: A product defect detection method, device and system are disclosed. The product defect detection method comprises: constructing a defect detection framework including a classification network, a localization and detection network, and a judgment network, and setting a quantity of the localization and detection network and judgment rules of the judgment network according to classification results of the classification network, wherein each localization and detection network is associated with a classification result, and each judgment rule is associated with a detection result of the localization and detection network; when performing product defect detection, inputting a product image acquired into the defect detection framework, using the classification network to classify defect types in the product image, detecting defects of the product image according to a localization and detection network associated with a classification result, then judging whether the product has a defect, and detecting a defect type and a defect position.
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公开(公告)号：US20210374941A1
公开(公告)日：2021-12-02
申请号：US17250263
申请日：2020-08-26
Applicant: GOERTEK INC.
Inventor： Jie LIU , Li MA , Liang ZHANG
Abstract: A product defect detection method, device and system are disclosed. The product defect detection method comprises: constructing a defect detection framework including a classification network, a locating detection network and a judgment network; training the classification network by using a sample image of a product containing different defect types to obtain a classification network capable of classifying the defect types existing in the sample image; training the locating detection network by using a sample image of a product containing different defect types to obtain a locating detection network capable of locating a position of each type of defect in the sample image; inputting an acquired product image into the defect detection framework, inputting a classification result and a detection result obtained into the judgment network to judge whether the product has a defect, and detecting a defect type and a defect position when the product has a defect.
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Patent Agency Ranking
公开(公告)号：US20220309635A1
公开(公告)日：2022-09-29
申请号：US17309306
申请日：2020-10-24
Applicant: GOERTEK INC.
Inventor： Fuli XIE , Yifan ZHANG , Jie LIU , Jifeng TIAN
Abstract: A computer vision-based anomaly detection method and device and an electronic apparatus are disclosed. The method comprises: dividing a target picture into at least two feature regions according to different region features of the target picture, and forming training sets respectively using the feature regions corresponding to each target picture; selecting generative adversarial networks GAN as network models to be used, and training GAN network models with the training sets of different feature regions to obtain GAN network models corresponding to different feature regions; and when performing anomaly detection, performing same feature region division on a target picture to be detected, inputting different feature regions of the target picture to be detected into corresponding GAN network models to obtain a generated picture, and performing pixel value-based difference detection on the generated picture and the target picture to be detected.
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公开(公告)号：US20210374940A1
公开(公告)日：2021-12-02
申请号：US17250262
申请日：2020-08-29
Applicant: GOERTEK INC.
Inventor： Jie LIU , Jifeng TIAN , Wenchao ZHANG , Yifan ZHANG
Abstract: A product defect detection method, device and system are disclosed. The method comprises: constructing a defect detection framework including segmentation networks, a concatenating network and a classification network, and setting a quantity of the segmentation network according to product defect types, wherein each segmentation network corresponds to a defect type; concatenating the sample image with the mask image output by each segmentation network by using the concatenating network to obtain a concatenated image; training the classification network by using the concatenated images to obtain a classification network capable of correctly identifying a product defect and a defect type; and when performing product defect detection, inputting a product image acquired into the defect detection framework, and detecting a product defect and a defect type existing in the product by using the segmentation networks, the concatenating network and the classification network.
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公开(公告)号：US20220309765A1
公开(公告)日：2022-09-29
申请号：US17309308
申请日：2020-10-30
Applicant: GOERTEK INC.
Inventor： Shunran DI , Yifan ZHANG , Jie LIU , Jifeng TIAN
IPC: G06V10/764 , G06V10/771 , G06V10/776 , G06V10/82 , G06N3/08
Abstract: The present disclosure discloses a method and device for optimizing an object-class model based on a neural network. The method includes: establishing the object-class model based on the neural network, training the object-class model, and realizing classification of target images by using the object-class model that has been trained; and when a new target image is generated, and the new target image is an image corresponding to a new condition of a target and is capable of still being classified into an original classification system, judging a result of identification of the object-class model to the new target image, and if the object-class model is not capable of correctly classifying the new target image, according to the new target image, selecting some of parameters, adjusting the some of parameters, and training to obtain an object-class model that is capable of correctly classifying the new target image.
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公开(公告)号：US20220309640A1
公开(公告)日：2022-09-29
申请号：US17309407
申请日：2020-08-26
Applicant: GOERTEK INC.
Inventor： Jie LIU , Jifeng TIAN , Fuli XIE , Shunran DI , Yifan ZHANG
Abstract: A product defect detection method, device and system are disclosed. The method comprises: acquiring a sample image of a product, extracting candidate image blocks probably including a product defect from the sample image, and extracting preset shape features corresponding to the candidate image blocks and texture features corresponding to the candidate image blocks; training a first-level classifier using the preset shape features to obtain a first-level classifier that can further screen out target image blocks probably including a product defect from the candidate image blocks; training a second-level classifier using the texture features to obtain a second-level classifier that can correctly identify a product defect; and when performing product defect detection, inputting preset shape features of candidate image blocks extracted from a product image into the first-level classifier, and then inputting texture features of obtained target image blocks into the second-level classifier to detect a defect in the product.
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公开(公告)号：US20220309764A1
公开(公告)日：2022-09-29
申请号：US17275800
申请日：2020-08-26
Applicant: GOERTEK INC.
Inventor： Jie LIU , Jifeng TIAN , Fuli XIE , Shunran DI , Yifan ZHANG
IPC: G06V10/764 , G06V10/82 , G06V10/26 , G06N3/08
Abstract: A method and device for small sample defect classification and a computing equipment are disclosed. The method comprises: separating a target to be tested into parts, and segmenting an original image of the target to be tested into at least two sub-images containing different parts according to the separated parts; establishing small sample classification models with respect to each sub-image and the original image respectively, and obtaining a classification result of each sub-image and a classification result of the original image by using corresponding classification models, wherein the classification result includes a defect category and a corresponding category probability; and determining and outputting a defect category of the target to be tested according to classification results of all the sub-images and the original image.
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