Piston ring detection method based on monocular vision detection

Abstract

The invention provides a piston ring closed-mouth gap and roundness zero-damage measurement method based on computer vision according to the computer vision theory, the image processing theory and mathematical knowledge and particularly relates to a piston ring detection method based on monocular vision detection. The method includes the steps of image enhancement, edge detection, roundness measurement, local sub-pixel processing, gap measurement and the like. According to the method, local image processing is conducted on a gap portion through the sub-pixel technology, so that the measurement accuracy is improved; the length of the gap is measured, and the roundness of a piston ring is measured, so the measurement range is expanded, and the detection quality is improved.

Description

technical field [0001] The invention relates to the detection field of automobile components, in particular to a method for detecting the quality of piston rings by using monocular vision. Background technique [0002] The piston ring is one of the important parts in the internal combustion engine, and it is also the key part for the piston to seal the gas and prevent the oil from entering the combustion chamber. The closed gap of the piston ring is the gap where the piston ring is placed in the cylinder. With the continuous improvement of industrial manufacturing technology and processing technology, enterprises have put forward higher and higher requirements for detection methods, detection speed and accuracy. However, the existing detection methods (such as calipers, microscopes, and coordinate measuring machines) are difficult to deal with the contradiction between speed and accuracy, and the measurement technology based on computer vision can solve this problem well. ...

AI Technical Summary

Problems solved by technology

However, the existing detection methods (such as calipers, microscopes, three-coordinate measuring instruments) are difficult to deal with the contradiction between speed and accuracy, and the measurement technology based on computer vision can solve this problem well

[0003] However, the existing algorithms for piston ring quality detection do not take into account sub-pixel data processing and roundness measurement of piston rings, which not only directly affects the measurement accuracy, but also affects the quality of detection

Images