Industrial part defect detection algorithm based on pixel vector invariant relation characteristic

Abstract

The invention relates to an industrial part defect detection algorithm based on a pixel vector invariant relation characteristic. The algorithm comprises the following steps that 1) contour extractionis carried out on a counter to be detected and local texture, and edge pixels to be detected are obtained; 2) the width of a detection window is defined, and is optimized according to a defined pixellinearity relation decision function, an inter-pixel direction vector is extracted from the window, the detection window is slid along the detection edge in a preset step length, and pixel vectors are extracted from all edges to be detected; and 3) an invariant relation characteristic of the pixel vectors of the edges to be detected is calculated, the invariant relation characteristic is comparedwith an inter-pixel invariant relation characteristic of a standard part, and whether the part has a defect is determined. The edge pixel vector is constructed by utilizing the difference in the local position relation of the contour pixels, difference matching is carried out by using invariance information between vector directions or vector module values, and defect detection is realized.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and automation, and in particular relates to an industrial part defect detection algorithm based on pixel vector invariant relationship features. Background technique [0002] With the development of science and technology, the automation of production process and the wide application of mechanical processing, the requirements for the precision, quality and processing efficiency of production and processing parts are constantly increasing. At the same time, the defect detection of parts has attracted more and more attention. Part defect detection is an important means to ensure the safety of parts. The traditional part damage detection method mainly relies on manual identification, which has the disadvantages of slow speed, troublesome data recording, and easy visual fatigue of the inspector. Moreover, the detection results are subjective, and it is difficult to guarantee the accuracy...

AI Technical Summary

Problems solved by technology

But when there are different scales in the size of the defect or the position of the part under test has a random angular offset between the position of the field of view and the template, how to define this measurement difference will be a difficult problem to solve

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