Wood board surface processing quality control method based on machine vision

Abstract

The invention discloses a wood board surface processing quality control method based on machine vision, relates to the field of machine vision, and is mainly used for furniture production defect detection. Comprising the steps of obtaining a board gray image; performing window sliding on the grayscale image, acquiring gradient direction features and direction inertia distance features of each window, dividing the gradient direction features and the direction inertia distance features into certain grades, and constructing a texture feature matrix of the corresponding window; calculating the filling degree of each window texture feature matrix; calculating a texture feature matrix filling degree mean value of the windows of different sizes, and obtaining the window of the optimal size; obtaining a defect texture noise reduction matrix of the window with the optimal size; fitting two dimensions of the defect texture noise reduction matrix, and calculating a wood grain stripping residue ratio; and plate surface machining defects are judged, and corresponding machining control is conducted according to the corresponding defects. According to the technical means provided by the invention, two defects of template processing can be accurately distinguished, and the detection efficiency and accuracy are improved.

Description

technical field [0001] The invention relates to the field of machine vision, in particular to a method for controlling the surface processing quality of wood boards based on machine vision. Background technique [0002] In the current wood products industry, wood panels are the basic product of the furniture production industry, and string-cut wood panels have become an important substrate type for furniture production due to their unique landscape and wood grain on the surface. This makes its surface quality directly affect the quality of finished furniture based on it. In addition to the defects of the original wood itself, the surface defects of string-cut wood are mostly surface processing defects caused by inaccurate setting of processing parameters during processing or hardware damage such as cutting tool wear. Therefore, it is necessary to control the surface quality of string-cut wood after cutting to ensure the quality of subsequent furniture products. [0003] Am...

AI Technical Summary

Problems solved by technology

[0004] The external manifestations of the above two defects are complex fine textures, and the formation process is similar, both are the process of peeling wood fibers from the surface, only the degree of peeling is different

In the existing technology, manual detection is prone to fatigue, and it is prone to false detection and missed detection in the rapid production of modern industry

The artificial neural network algorithm requires a large amount of training data, and the cost of data acquisition and labeling is high. It is difficult for existing machine vision technology to accurately distinguish the two types of defects in the case of complex textures. Therefore, a detection method that can distinguish the above two types of defects is needed. To provide adjustment basis for subsequent quality control measures

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