Multi-scale ceramic detection method and system

Abstract

The invention provides a multi-scale ceramic detection method and system. The method comprises the steps that: rapid detection is carried out on a target object, a workpiece is placed on a workbench, and photographing and detection are performed on the workpiece through a rapid detection system; and when flaws do not exist, detection is completed, a conclusion that the flaws do not exist is given, and when the flaws exist, accurate detection including a three-dimensional reconstruction technology, a trajectory planning technology and the like is adopted, so that quantitative flaw indexes are given. Compared with a traditional method, the multi-scale detection method combining rapid detection and accurate detection has the following advantages that: the influence of environmental factors on a detection result is reduced to a great extent; and the detection accuracy is effectively improved. Compared with a deep learning method, the method does not need to spend a large amount of time and cost to complete acquisition of a required data set.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a multi-scale ceramic detection method and system. Background technique [0002] The ceramic industry is an important part of my country's national economy. The scale of my country's ceramic industry ranks first in the world, with the highest production, export and consumption of ceramic products in the world, and has a very large market prospect. In recent years, the production technology, mechanization, and automation of the ceramic industry have gradually improved, and a large number of automated production equipment and production lines have been introduced. Semi-automatic production has been realized from mud refining, billet drawing, billet sharpening, billet drying, glazing to kiln firing. However, the quality inspection and grading evaluation process has not yet been automated. It mainly relies on human eye observation and auxiliary touch for inspection and evaluation. No...

AI Technical Summary

Problems solved by technology

Among them, the ceramic defect detection method based on the traditional method mainly uses a camera to shoot the target object, completes the acquisition of its color and depth information, and then uses a visual algorithm to identify and mark the defect, so it has the disadvantage of being greatly affected by external environmental factors , the reliability of the obtained results is not high; compared with the traditional method, the accuracy of the ceramic flaw detection method based on the neural network is higher, but it requires a large amount of data sets to train the corresponding network, which consumes too much time and is too expensive

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