Binary image connected domain labeling method for industrial product surface defect detection

Abstract

A binary image connected domain labeling method for industrial product surface defect detection is provided, comprising: firstly, acquiring a gray level image of a product surface and performing denoising processing, and converting the gray level image into a binary image; then, performing first scanning on the binary image in a manner of scanning once for every three rows from left to right and from up to down, labeling three pixels each time, at the same time of labeling, combining equivalent connected domains and recording representation labels of the connected domains at any time; and finally, performing second scanning on the labeled image, and at the time of performing the second image scanning, duplicating each pixel in a corresponding connected domain by using a representation label of the corresponding connected domain. In a whole process, the number of repeatedly checking pixels is minimized, so that efficiency of a binary image connected domain labeling algorithm is improved; efficiency of detecting a product surface defect is improved, and a requirement of industrial product surface defect detection for a speed is satisfied. Therefore, the method has characteristics of being high in efficiency and strong in practicability.

Description

technical field [0001] The invention belongs to the technical field of detection of surface defects of industrial products, and in particular relates to a method for marking connected domains of binary images oriented to detection of surface defects of industrial products. Background technique [0002] In the process of industrial production, people hope that the quality of non-destructive testing products can be synchronized on the high-speed production line, for example, whether there is paper defect on the product on the paper machine at a kilometer per minute, and whether the quality of the printed product is up to standard on the high-speed printing machine , Automatically detect whether there are breaks and short circuits in the high-speed circuit board production line. In these cases, detecting objects in images and extracting their object features are indispensable processes. [0003] To extract the target and its features in the image, the pixels in the image must ...

AI Technical Summary

Problems solved by technology

This method often needs to deal with a large number of repeated marks, the logic is complex, and the efficiency is low. However, the real-time requirements of industrial product defect detection are relatively high, so it cannot meet the needs of industrial production.

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