Sub-pixel edge detection of electron beam welded parts based on machine vision

Haining Hu; Leiyang Huang

doi:10.1117/12.3051610

18 November 2024 Sub-pixel edge detection of electron beam welded parts based on machine vision

Haining Hu, Leiyang Huang

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134033O (2024) https://doi.org/10.1117/12.3051610
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

A sub-pixel edge detection method based on improved polynomial interpolation is proposed to solve the problem of rapid positioning and real-time detection of the position and welding offset of the workpiece during electron beam welding. In the process of image processing, the Canny edge detection algorithm is first used for rough localization. This algorithm effectively extracts the edge information of the weld seam through Gaussian filtering, gradient calculation, non maximum suppression, dual threshold detection, and edge connection steps. In order to improve the accuracy of edge detection, an adaptive adjustment method of dynamic threshold is introduced to adapt to images with different brightness and contrast. In the edge refinement stage, polynomial interpolation is used for sub-pixel coordinate detection. By using cubic interpolation method and combining the grayscale information of local areas, the sub-pixel positions of edges are accurately calculated, thereby improving the accuracy of edge detection. The experimental results show that the improved method has significant accuracy advantages in edge extraction and sub-pixel coordinate detection, providing an effective solution for quality control of electron beam welding.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haining Hu and Leiyang Huang "Sub-pixel edge detection of electron beam welded parts based on machine vision", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134033O (18 November 2024); https://doi.org/10.1117/12.3051610

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