Magnetic tile surface defect detection method based on improved machine vision attention mechanism

A technology of attention mechanism and defect detection, applied in optical testing flaws/defects, instruments, image data processing, etc., can solve problems such as insufficient detection accuracy and speed, and achieve good adaptability, high reliability, and high algorithm accuracy. Effect

Active Publication Date: 2016-11-09
ANHUI UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

[0009] Aiming at the problem that the existing magnetic tile surface defect detection algorithm has insufficient detection accuracy and speed, the present invention p

Method used

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  • Magnetic tile surface defect detection method based on improved machine vision attention mechanism
  • Magnetic tile surface defect detection method based on improved machine vision attention mechanism
  • Magnetic tile surface defect detection method based on improved machine vision attention mechanism

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Embodiment 1

[0066] combine figure 1 , a method for detecting surface defects of magnetic tiles based on an improved machine vision attention mechanism in this embodiment includes the following steps:

[0067] Step 1: Input the original image of the surface of the magnetic tile. During image processing, some areas with low gray values ​​are easily confused with defect areas, which interferes with the processing results. This embodiment proposes a method of combining the top-hat and bottom-hat transformations of morphology to improve the contrast between the defect area and the background, suppress the gray value of the high-brightness area, enhance the overall gray contrast of the image, and facilitate the identification of the defect area .

[0068] The process of performing top-hat and bottom-hat transformations is:

[0069] Top hat transformation graph T hat (f) is the opening operation γ(f) of the image subtracted from the original image f, and the bottom hat transformation map B h...

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Abstract

The invention discloses a magnetic tile surface defect detection method based on an improved machine vision attention mechanism. The magnetic tile surface defect detection method comprises the following steps: I, inputting a magnetic tile image, and enhancing the overall gray contrast ratio of the image by using a method of combination of morphological top cap and bottom cap conversion; II, uniformly dividing the obtained image into a*b image blocks, and distinguishing defect image blocks and non-defect image blocks according to gray characteristic quantities of the divided image blocks; III, calculating the conspicuousness of an obtained image block by using an improved Itti vision attention mechanism model, and selecting a primary characteristic so as to form a comprehensive saliency map; and IV, thresholding the comprehensive saliency map by using an Ostu threshold method, and extracting a defect area. By virtue of morphological processing, image blocking and vision attention mechanism ideas, problems that the brightness is not uniform, the magnetic tile defect area is relatively small, a magnetic tile has texture interference and the like can be effectively overcome, various magnetic tile defects can be rapidly and effectively extracted, and thus the magnetic tile surface defect detection method is very good in adaptability.

Description

technical field [0001] The invention relates to the technical field of machine vision, in particular to a method for detecting surface defects of magnetic tiles based on an improved machine vision attention mechanism. Background technique [0002] Ferrite magnet tile is a kind of tile-shaped magnet mainly used in permanent magnet motors, and its quality directly affects the overall performance of permanent magnet motors. During the production process of magnetic tiles, due to technological problems, the surface of magnetic tiles is prone to defects such as cracks, damage, pitting, etc., which directly affects the normal use of magnetic tiles. At present, the judgment of the surface defects of magnetic tiles in industrial production basically adopts manual detection, which has poor detection accuracy, low detection efficiency and high labor cost. [0003] With the continuous development of machine vision, defect detection technology based on machine vision has begun to be wi...

Claims

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Application Information

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IPC IPC(8): G01N21/95G06T7/00G06T5/00
CPCG01N21/95G06T5/00G06T7/0008G06T2207/20036G06T2207/30136
Inventor 李丹孙海涛陆晓燕
Owner ANHUI UNIVERSITY OF TECHNOLOGY
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