Defect detecting method based on gradient multiple threshold value optimization

Abstract

The invention discloses a defect detecting method based on gradient multiple threshold value optimization. Firstly, an optimization threshold value is calculated through a simplified mean value clustering algorithm; next, statistic is performed on 100 modules in each sample gradient image through a normal distribution model, and a dynamic threshold is calculated and obtained; then, through partitioning processing on the sample images, based on a statistical method, a pixel maximum value and a pixel difference maximum value are extracted from each module; finally, on the basis of modularization, judgment is conducted through the multiple threshold values, the output modules are obtained and combined into a complete image, and median filtering is conducted on the image to obtain a defect detection result image. According to the defect detecting method based on the gradient multiple threshold value optimization, through the simplified mean value clustering algorithm, the accuracy of the algorithm is improved, and the time cost of the algorithm in the iterative process is reduced; based on statistics and the normal distribution model, edges are extracted from the gradient image, and the accuracy and the processing effect of the algorithm are remarkably increased. The defect detecting method based on the gradient multiple threshold value optimization can rapidly and accurately detect defects of wood, and the detection application range and the quality of produced wood are improved.

Description

technical field [0001] The invention relates to a defect detection method based on gradient multi-threshold optimization. Background technique [0002] In image segmentation processing, an image is divided into components that have a strong correlation with real-world objects or regions, which are mainly divided into the following three categories: global-based segmentation, edge-based segmentation, and local-based segmentation. At present, image segmentation is mainly used for the fine extraction of image edges. For the detection of strong edges, it mainly focuses on global segmentation and local segmentation, that is, thresholding segmentation. However, for some specific occasions, such as wood defect detection, etc., in addition to filtering In addition to removing the noise of the image itself, it is also necessary to remove the annual rings of the wood, etc., and retain the defective parts of the wood. Thresholding segmentation is an ideal method for background and obj...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a defect detection method based on gradient multi-threshold optimization, which is used to solve problems such as inaccurate thresholds, thresholds that cannot change with image fluctuations, too long iterations of optimal thresholding, and complex algorithms, etc., and has defect detection effects Good, fast extraction speed, etc.

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