Solid wood floor surface defect detecting method based on image fusion and division

Abstract

The invention discloses a solid wood floor surface defect detecting method based on image fusion and division, and relates to the field of floor surface defect detecting. Aiming at the problems that due to the fact that division of a region growing algorithm is low in speed and inaccurate, solid wood floor surface defect detecting is low in speed and accuracy, and quality and sorting grades of solid wood floors are affected, the solid wood floor surface defect detecting method includes the steps that an R component image of defects is extracted and shrunk, and a region growing method is used in a low-dimensional image space to rapidly position the defects; the shrunk image is amplified and recovered by using gradient information interpolations, and the defects are marked to generate a reference image; the edges of the reference image are retrieved and marked through wavelet transform, and taboo quick search is performed on an original image with edge pixel points as seeds to rapidly and accurately divide defect regions. Defect detecting tests are performed on 20 sample images containing live knots, dead knots and cracks, the average dividing time is 13.21 ms, and the accuracy rate of defect division regions reaches 96.8%.

Description

technical field [0001] The invention relates to a method for detecting surface defects of a solid wood floor, and relates to the field of detection of floor surface defects. Background technique [0002] The surface defects of solid wood floor directly affect the product grade. The detection of surface defects of solid wood floor based on computer vision has important practical significance for the automatic sorting of wooden floor [1-2] . The computer vision inspection system for solid wood flooring first collects the information of the surface image by the camera; then, uses the segmentation algorithm to detect the surface defect area; finally, uses the classification algorithm to judge the defect type of the defect area [3-5] . Commonly used color processing models are RGB model, HSV model and HSI model [6] ; Considering the processing time, it is more suitable as a hardware-oriented RGB model. The previous experimental research experiments show that the R component im...

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Problems solved by technology

[0020] The present invention aims at the problems of slow segmentation speed and inaccurate segmentation existing in the region growing algorithm, resulting in slow detection speed and low precision of surface defects of solid wood flooring, thereby affecting its quality and sorting level; therefore, a segmentation method based on image fusion is proposed. Surface defect detection method of solid wood flooring (defect localization method based on image fusion)

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