Gear broken tooth detection method based on Hough circle detection

Abstract

The invention relates to the technical field of data processing, in particular to a gear broken tooth detection method based on Hough circle detection. According to the method, a pixel code of each line is obtained through line-by-line analysis of a gear binary image; and obtaining a second pixel value sequence corresponding to the pixel code which is the same as the standard code. According to the gear center hole pixel value position in the second pixel value sequence, hole edge pixel points are obtained, and then the circle center is obtained. Performing Hough circle detection on the circle center to obtain a plurality of initial reference circles, and screening out reference circles according to the number of gear information on the edges of the initial reference circles. According to the number of the pixel points of the gear information and the background information on the edge of the reference circle, whether tooth breakage occurs is judged. According to the method, the edge acquisition process is simplified in pattern recognition by combining related electronic equipment, and the detection of the position and degree of the broken tooth is quickly and effectively realized through the analysis of the pixel value on the reference circle.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a gear broken tooth detection method based on Hough circle detection. Background technique [0002] Gears are the basic parts of machinery and equipment in various fields. In the process of parts processing, gear teeth may be broken due to process defects, transportation errors and other factors. Gears with broken teeth will greatly affect the operation accuracy and service life of equipment and instruments, so gears need to be detected for broken teeth in the process of gear production. [0003] Because the broken tooth defect of the gear is relatively obvious, the image features of the gear can be extracted by the method of computer vision, and whether there is a broken tooth defect can be judged according to the image features. The common prior art can use edge extraction algorithm to extract edge information of gear image, and judge whether there is broken tooth thro...

AI Technical Summary

Problems solved by technology

However, both of these algorithms require a lot of image processing. Both edge detection and neural network training require a large amount of data processing. For large-scale gear broken teeth detection, it is impossible to quickly and smoothly obtain broken teeth information.

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