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Machine Vision-Based Precision Detection of Circular Holes Using Canny Threshold Optimization and Zernike Moments.

Juan Du¹, Jizheng Yu¹, Xintian Jiang¹

¹School of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252000, China.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

This study introduces an automated system for precise circular hole measurement in sheet metal, improving accuracy beyond industrial standards. The method optimizes Canny edge detection and uses Zernike moments for enhanced quality inspection.

Keywords:

Canny operator Zernike moments algorithm optimization circular hole detection subpixel

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Area of Science:

Industrial Metrology
Computer Vision
Image Processing

Background:

Manual inspection of circular holes in sheet metal suffers from low accuracy.
Automated inspection systems are needed to improve precision and efficiency in industrial testing.

Purpose of the Study:

To develop a precision detection method for automated measurement of circular holes in sheet metal.
To enhance the accuracy and reliability of industrial quality inspection processes.

Main Methods:

Developed an automated measurement system using MATLAB.
Employed adaptive median filtering for image preprocessing, validated by Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM).
Utilized Otsu's thresholding for segmentation, an adaptive Canny operator threshold strategy for edge detection, and Zernike moments with sub-pixel localization for precise edge point identification.

Main Results:

Achieved measurement repeatability with a standard deviation below 0.02 mm.
Controlled absolute error within ±0.05 mm.
Exceeded the industrial precision requirement of ±0.3 mm.

Conclusions:

The proposed method offers an effective solution for automated quality inspection of sheet metal hole manufacturing.
The integration of Canny operator threshold optimization and Zernike moments significantly improves measurement accuracy.
The developed system provides a reliable and precise alternative to manual inspection methods.