Title:
欠点検査装置および学習済みモデル
Document Type and Number:
Japanese Patent JP7298176
Kind Code:
B2
Abstract:
To provide a fault inspection device, fault inspection method, fault inspection program, learning device and learned model that can highly accurately detect a fault even if a fault size is quite small in an inspection image acquired from a product targeted for an inspection.SOLUTION: A fault inspection device comprises: an image processing unit that inputs data on an image data to a learned model created by machine learning using a first image group made of an image in which an inspection-purpose image includes a fault of a pixel and a second image group made of an image not including the fault, and for repairing only the fault included in the image, performs a computation based on the learned model, and thereby outputs data on an output image; a calculation unit that compares regions corresponding to the input image and output image to thereby calculate difference information quantifying a difference between both image; and a determination unit that determines whether the input image includes the fault or not on the basis of the difference information calculated by the calculation unit.SELECTED DRAWING: Figure 2
Inventors:
Shingo Mizuta
Shinji Okaya
Shinji Okaya
Application Number:
JP2019023830A
Publication Date:
June 27, 2023
Filing Date:
February 13, 2019
Export Citation:
Assignee:
Toray Industries, Inc.
International Classes:
G01N21/93; G06T7/00
Domestic Patent References:
| JP2018205163A | ||||
| JP2018005773A | ||||
| JP5228511A | ||||
| JP2018181389A |
Other References:
進藤 智則,教師なしディープラーニングで製造不良品を自動検出 武蔵精密工業が自動車ギア検査にautoencoder,日経Robotics 2018年5月号(第34号),日本,2018年04月10日
三浦 勝司 外2名,違和感を察知するDeep Learning技術”Sense Learning”,SEIテクニカルビュー 第193号,日本,住友電気工業株式会社,2018年07月31日
Toby P. Breckon et al.,Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection,2019 International Joint Conference on Neural Networks (IJCNN),米国,2019年07月19日,https://arxiv,org/abs/1901.08954
三浦 勝司 外2名,違和感を察知するDeep Learning技術”Sense Learning”,SEIテクニカルビュー 第193号,日本,住友電気工業株式会社,2018年07月31日
Toby P. Breckon et al.,Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection,2019 International Joint Conference on Neural Networks (IJCNN),米国,2019年07月19日,https://arxiv,org/abs/1901.08954
Attorney, Agent or Firm:
Sakai International Patent Office
Previous Patent: System and method for field device management using class parameter sets
Next Patent: MOVING PICTURE/STILL PICTURE RECORDER
Next Patent: MOVING PICTURE/STILL PICTURE RECORDER