Utechzone Co., Ltd.

Arulmurugan Ambikapathi

Chien-Chung Lin

Chia-Liang Lu

Chih-Heng Fang

Ming-Tang Hsu

Chia-Chun Tsou

Cheng-Hua Hsieh

The present invention provides an automated optical inspection and classification apparatus based on a deep learning system, comprising a camera and a processor. The processor executes a deep learning system after loading data from a storage unit and the processor, and comprises an input layer, a neural network layer group, and a fully connected-layer group. The neural network layer group is for extracting to an input image and thereby obtaining a plurality of image features. The fully connected-layer group is for performing weight-based classification and outputting an inspection result.

Automated optical inspection and classification apparatus based on a deep learning system and training apparatus thereof

An optical inspection method for an optical inspection device comprising an optical lens is provided according to an embodiment of the disclosure. The optical inspection method includes: obtaining a first image of an object by the optical lens; performing an edge detection on the first image to obtain a second image comprising an edge pattern; and performing a defect inspection operation on the second image based on a neural network architecture to inspect a defect pattern in the second image. In addition, an optical inspection device and an optical inspection system are provided according to embodiments of the disclosure.

Defect inspection method, defect inspection device and defect inspection system

A defect inspection system, connected to an automatic visual inspection device, is provided, including the followings. A re-inspection server (VRS) receives a defect image and a defect location. A training terminal stores trained modules. A classification terminal receives the defect image and the defect location, reads a target trained module corresponding to the defect image, classifies the defect image according to the target trained module to obtain a labeled defect image, and sends the labeled defect image to the VRS. A re-inspection terminal receives the labeled defect image from the VRS, and sends a verified operation corresponding to the labeled defect image to the VRS. A labeling re-inspection terminal receives the verified operation and the labeled defect image, and a labeling result corresponding to the labeled defect image. The VRS sends the labeling result and the labeled defect image to the training terminal to train a corresponding training module.

Defect inspection system and method using artificial intelligence

The present invention is an automated optical inspection method using deep learning, comprising the steps of: providing a plurality of paired image combinations, wherein each said paired image combination includes at least one defect-free image and at least one defect-containing image corresponding to the defect-free image; providing a convolutional neural network to start a training mode of the convolutional neural network; inputting the plurality of paired image combinations into the convolutional neural network, and adjusting a weight of at least one fully connected layer of the convolutional neural network through backpropagation to complete the training mode of the convolutional neural network; and performing an optical inspection process using the trained convolutional neural network.

Automated optical inspection method using deep learning and apparatus, computer program for performing the method, computer-readable storage medium storing the computer program, and deep learning system thereof

A method for evaluating an efficiency of a manual inspection for a defect pattern is provided according to an embodiment of the disclosure, which comprises: enabling an evaluation program; loading a test image automatically by the enabled evaluation program and displaying the test image in a user interface; detecting a user behavior of a user after the user watches the test image; generating original data according to the user behavior, wherein the original data reflects at least one of whether the user identifies the defect pattern in the test image and a type of the defect pattern identified by the user; and performing a quantitative operation on the original data to generate evaluation data corresponding to the efficiency of the manual inspection, wherein the evaluation data reflects an evaluation result corresponding to the efficiency of the manual inspection.

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Taipei, Taiwan

Arulmurugan Ambikapathi