Convolutional neural network and active learning-based ceramic tile surface defect recognition method

A convolutional neural network, active learning technology, applied in character and pattern recognition, image data processing, instruments, etc., can solve the problem of high time complexity, achieve the effect of accelerating convergence and reducing labeling costs

Active Publication Date: 2018-05-15
ZHEJIANG UNIV OF TECH
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The flow-based method means that unlabeled samples enter the classifier one by one, and the c

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  • Convolutional neural network and active learning-based ceramic tile surface defect recognition method
  • Convolutional neural network and active learning-based ceramic tile surface defect recognition method
  • Convolutional neural network and active learning-based ceramic tile surface defect recognition method

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[0038] The present invention provides a ceramic tile surface defect detection method based on convolutional neural network and active learning. In order to further illustrate the technical solution of the present invention, examples are given below in conjunction with the accompanying drawings. It should be understood that the examples given here are only used to explain the present invention, not to limit the present invention.

[0039] Such as figure 1 As shown, a tile surface defect detection method based on convolutional neural network and active learning includes the following steps:

[0040] (1) Obtain images and preprocess: acquire surface images of tiles with defects, the number should not be less than 1000, and perform preprocessing.

[0041] (2) The establishment of the training set: the preprocessed tile surface image obtains more image blocks through the sliding window method, and its set is U, which is divided into 5 parts equally, that is, U={U 1 ,U 2 ,U 3 ,U...

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Abstract

The invention discloses a convolutional neural network and active learning-based ceramic tile surface defect recognition method. The method comprises the following steps of: (1) obtaining and preprocessing an image; (2) establishing a training set; (3) establishing and training a convolutional neural network; (4) carrying out active learning; (5) carrying out model iteration; and (6) carrying outonline detection. Compared with the prior art, the method has the advantages that (1) features of ceramic tile surface defects are automatically extracted by using the convolutional neural network, sothat good priori knowledges are not required in the aspect of defect feature extraction, and multiple defect types in one to-be-detected image can be recognized; and (2) active learning is imported in convolutional neural network training, so that labeling cost of samples is effectively decreased and the model convergence is accelerated.

Description

technical field [0001] The invention belongs to the technical field of defect detection and recognition, and more specifically relates to a tile surface defect recognition method based on convolutional neural network and active learning. Background technique [0002] In the production process of ceramic wall and floor tiles, due to improper manufacturing processes or collisions during handling, defects such as lack of glaze, cracks, and scratches will appear on the surface of the tiles, which will affect the aesthetics of the tiles as building decoration materials. The detection of surface defects is still largely done manually. This method is not only inefficient, but also easily affected by the intuitive feelings of the inspectors. In recent years, the automatic detection of product surface defects using machine vision has been paid more and more attention. [0003] In the prior art, there are a few tile surface defect detection methods, which mainly use manual selection a...

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Application Information

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IPC IPC(8): G06T7/00G06T7/13G06T7/168G06K9/62
CPCG06T7/0008G06T7/13G06T7/168G06T2207/20084G06T2207/20081G06T2207/20061G06T2207/30108G06F18/2414G06F18/214
Inventor 姚明海黄展聪
Owner ZHEJIANG UNIV OF TECH
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