Hardware defect classification recognition method based on depth convolution neural network

A neural network and deep convolution technology, applied in the field of image processing, can solve problems such as the accuracy of defect region segmentation, the complexity of accurate segmentation, and noise interference, so as to avoid falling into local minimum, reduce training time, and fast convergence Effect

Inactive Publication Date: 2017-12-15
GUANGDONG UNIV OF TECH
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Problems solved by technology

It can be seen that the above-mentioned traditional defect recognition algorithm is very dependent on the accuracy of defect region segmentation, and needs to manually select and extract defect features
However, f

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  • Hardware defect classification recognition method based on depth convolution neural network
  • Hardware defect classification recognition method based on depth convolution neural network
  • Hardware defect classification recognition method based on depth convolution neural network

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Embodiment Construction

[0054] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and through specific embodiments.

[0055] A method for classifying and identifying hardware defects based on a deep convolutional neural network, comprising the following steps:

[0056] A. Establish a network and construct a deep convolutional neural network;

[0057] B. train the network, divide the defect images collected into two categories, i.e. a training set and a test set, the training set accounts for 70% of the total number of images collected, and the test set accounts for 30% of the total number of images collected;

[0058] C. Defect recognition, input the hardware images in the test set into the trained network, check the output results, compare the recognition results with the labels of the images, and count the correct recognition rate and wrong recognition rate.

[0059] To further illustrate, the training network algorithm de...

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Abstract

The invention discloses a hardware defect classification recognition method based on a depth convolution neural network, and the method comprises the steps: building a network: constructing the depth convolution neural network; training the network: classifying collected images into two classes: a training set and a test set, wherein the training set comprises 70% of the collected images, and the test set comprises 30% of the collected images; recognizing defects: inputting a hardware image in the test set into the trained sample, inspecting the output result, contrasting a recognition result and the label of the image, and carrying out the statistics of the correct recognition rate and wrong recognition rate. The method employs the depth convolution neural network, saves a complex image processing algorithm, achieves the extraction of more abstract features of defects through the adding of a network depth, is stronger in distinguishability between different defect classes, and is higher in recognition rate.

Description

technical field [0001] The invention relates to the technical field of image processing based on artificial intelligence, in particular to a hardware defect classification and recognition method based on a deep convolutional neural network. Background technique [0002] Machine vision, also known as computer vision, is a science that studies the use of cameras and computers to imitate human eyes and brains, so that machines can replace humans for detection and judgment, and complete tasks such as target recognition and industrial inspection. Machine vision technology is an applied technical discipline that integrates digital image processing, artificial intelligence, computer graphics and other disciplines, and is widely used in automated production. In recent years, with the advancement of computer technology and the continuous improvement of neural network theory, the rapid development of computer vision has been promoted. The rapid development of my country's machine vis...

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Application Information

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IPC IPC(8): G06T7/00G06N3/08G06N3/04G06K9/62
CPCG06N3/08G06T7/0004G06T2207/30164G06T2207/20081G06T2207/20084G06N3/048G06F18/24G06F18/214
Inventor 王宏杰李海艳黄运保
Owner GUANGDONG UNIV OF TECH
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