Cascade quick image defect segmentation method

Abstract

The invention relates to a cascade quick image defect segmentation method. The method includes: preprocessing an image to acquire global threshold information, and performing rough image segmentation based on a global adaptive threshold; acquiring fine image segmentation through priori knowledge and a level set method of local image information; eliminating evident error candidate defects and error alarms by means of size and shape analysis, and performing target separation on the basis of a parameterized mathematical morphology method. the problems such that non-uniformity of local and global thresholds leads to inaccurate image segmentation are solved by means of a stratified strategy; features of defects, such as shape and size, are introduced to a level set energy function; the priori knowledge of the defect shape is fully utilized, the problem of initial dependence is solved, and the method is well applicable to image defect segmentation having shape features; by introducing the cascade image processing architecture and discarding most non-defective images from the most front, unbalance of data distribution is greatly relieved, and defect segmentation speed and precision is greatly improved.

Description

technical field [0001] The invention relates to an image processing technology, in particular to a cascade fast image defect segmentation method. Background technique [0002] Segmenting defect targets from images with uneven gray levels is a difficult and important technology, and it is also a key step in subsequent detection and classification. The introduction of human prior knowledge of various defects can have great advantages in the segmentation of defect images , especially surface anomaly detection of industrial products and pipeline defect detection. The usual practice is to use a global adaptive threshold image segmentation method, such as the Otsu method to determine the optimal gray-level segmentation threshold by maximizing the variance between classes and minimizing the variance within the class, but the unevenness of the gray level makes the selection of local thresholds inappropriate. , it is difficult to obtain satisfactory segmentation results; on the othe...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem of time-consuming and low accuracy in segmenting defects from images with uneven gray levels, the present invention proposes a cascaded fast image

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