Technical gazette, Vol. 33 No. 3, 2026.
Original scientific paper
https://doi.org/10.17559/TV-20250612002743
Weld Seam Identification Using Edge Detection in Machine Vision
Chenlei Zhao
; Geely University of China, Chengdu 641423, China
Dong Wu
; Geely University of China, Chengdu 641423, China
Lin Xi
; Geely University of China, Chengdu 641423, China
Lihong Guo
; Chengdu Jincheng College, Chengdu 611731, China
*
Shenghong Wu
; Sichuan Technology & Business College, Chengdu 611830, China
Xiao Luo
; Geely University of China, Chengdu 641423, China
Shunyang Hu
; Hosei University, Tokyo 1638001, Japan
Yiran Ding
; Geely University of China, Chengdu 641423, China
* Corresponding author.
Abstract
In intelligent welding, achieving the automation of weld quality inspection is a significant challenge, and weld seam marking is of crucial importance. For this purpose, a method based on edge detection using binary image preprocessing was developed on the MATLAB platform. Compared with the traditional multi-sensor fusion approach, this method does not require complex sensor integration, simplifying the implementation process. Compared with neural network methods, it is more flexible and simpler. The method first preprocesses the image into a binary image and then compares the weld seam feature marking with the Roberts, Prewitt, Sobel, and Canny operators. The results show that the Canny operator demonstrates a significant performance advantage in the comparison of four indicators: point sharpness, entropy, average gradient, and Quality Assessment of Blended Features. Its performance is 3 to 25 times that of other operators, and it performs best in weld seam feature texture detection, showing high robustness.
Keywords
edge detection; grayscale conversion; machine vision technology; weld seam marking
Hrčak ID:
346735
URI
Publication date:
30.4.2026.
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