Noncontact High-Precision Aperture Detection Based on Distortion Compensation and Adaptive Mean Blur Algorithm.

High-precision apertures are of great significance for the manufacture and assembly of precision instruments. There are currently two common types of methods for aperture measurement: manual measurement methods and computer vision-based methods. The computer vision-based methods work by using industrial cameras, morphological operators, and image interpolation algorithms and can dramatically reduce human errors; however, these methods still suffer from two problems. First, common morphological operations would damage the edge information of the image and fail to capture the accurate edge of the hole. Second, although the magnification algorithm based on image interpolation has an obvious edge positioning effect, it tends to make the edge jagged and blurred. To reduce the influence of image distortion on the error of subsequent image processing, we perform distortion compensation on the image and use the grid intersection and pixel information in the vicinity for fitting and analysis, and calculate the lens distortion coefficient to correct the image distortion. Then, to obtain more accurate edge information, we improve the Canny operator by using the mean blur with an adaptive threshold that preserves the edge in place of the Gaussian blur and adding a new convolution kernel to smooth the image to provide a more accurate profile for subsequent aperture measurements. With these efforts, we propose a noncontact high-precision aperture measurement method based on a distortion compensation and adaptive mean blur algorithm. The proposed method has a measurement error no greater than +/- 0.03 mm and achieves a 100% pass rate. The method solves the problem that the edge contour of the hole cannot be accurately extracted due to edge blurring and allows for high-precision detection of apertures in industrial fields. The method is expected to be used with a number of precision instruments for aperture size measurement.