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Weld Seam Identification Using Edge Detection in Machine Vision.

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Title: Weld Seam Identification Using Edge Detection in Machine Vision.
Authors: ZHAO, Chenlei1 zhaochenlei@stu.xhu.edu.cn, WU, Dong1 447325098@qq.com, XI, Lin1 lishang_1880@163.com, GUO, Lihong2 guo_lihong123@126.com, WU, Shenghong3 1529432209@qq.com, LUO, Xiao4 535292271@qq.com, HU, Shunyang5 kojunyou@gmail.com, DING, Yiran6 3180900037@qq.com
Source: Technical Gazette / Tehnički Vjesnik. 2026, Vol. 33 Issue 3, p1244-1252. 9p.
Subjects: Edge detection (Image processing), Welding inspection, Image enhancement (Imaging systems), MatLab (Computer software), Computer vision, Image processing
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. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
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. [ABSTRACT FROM AUTHOR]
ISSN:13303651
DOI:10.17559/TV-20250612002743