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A method for detecting the quality of decorative patterns on the surface of architectural ceramics

A quality inspection method and technology for building ceramics, applied in image enhancement, image analysis, image data processing, etc., to achieve high detection accuracy, high detection accuracy, and labor-saving effects

Active Publication Date: 2017-12-26
童垸林
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] At present, there is no public literature on the method of automatic detection of pattern quality on the surface of architectural ceramics based on salient object detection of deep neural network at home and abroad.

Method used

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  • A method for detecting the quality of decorative patterns on the surface of architectural ceramics
  • A method for detecting the quality of decorative patterns on the surface of architectural ceramics
  • A method for detecting the quality of decorative patterns on the surface of architectural ceramics

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Embodiment 1

[0075] Example 1: see figure 1 As shown, a method for detecting the quality of patterns on the surface of architectural ceramics includes the following steps:

[0076] (1) Extract the salient features of the test sample:

[0077] (1) 500 image samples of building ceramic products to be tested, such as figure 2 , Calculate its visual saliency map, such as image 3 . The saliency model adopts a two-level strategy from coarse to fine. In the first level, it is proposed to merge the contrast prior knowledge saliency map, the central prior saliency map and the boundary prior saliency map to obtain the rough saliency map. In the second level, the background energy term based on the boundary prior knowledge, the data energy term based on the rough saliency map and the smooth energy term based on the smooth prior are combined to propose a new energy equation:

[0078]

[0079] Where i represents the i-th superpixel in the image, N represents the number of superpixels in the image, S i Re...

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Abstract

The invention belongs to a building ceramic surface pattern quality detection method, comprising: combining a visual sense saliency model and a convolutional neural network model to automatically detective the saliency area of a building ceramic surface pattern; employing Grabcut to automatically divide a surface pattern; utilizing the convolutional neural network model to detect the quality of the surface pattern, thereby realizing high detection accuracy. The building ceramic surface pattern quality detection method can effectively detect building ceramic and colorful building ceramic surface pattern quality, save labor, and reduce labor intensity, and has high work efficiency and detection precision.

Description

Technical field [0001] The invention belongs to a method for detecting the quality of patterns on the surface of architectural ceramics, and is particularly suitable for the detection of colored architectural ceramics. Background technique [0002] The surface quality testing of building ceramics in my country is still in its infancy, and the quality testing of the surface of building ceramics is mainly through manual measurement. Because the building ceramic products on the production line run very fast, some inspections can only be carried out by means of random inspections, resulting in a high probability of missed inspections and false inspections. Therefore, the intelligent detection of the surface quality of building ceramics is very important in the quality control of building ceramics. [0003] At present, most surface defect algorithms focus on the extraction of low-level features. They are all artificially designed feature extraction methods and cannot adapt to changes i...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00
CPCG06T7/0004G06T2207/30168
Inventor 童垸林张汗灵胡峰松
Owner 童垸林