Weld Cross-Section Profile Fitting and Geometric Dimension Measurement Method Based on Machine Vision

Abstract

A visual measurement method based on a key point detection network is proposed for the difficulty of fitting the cross-sectional profile of ultra-narrow gap welds and the low efficiency and accuracy of manual measurement of geometric parameters. First, the HRnet (High-Resolution Net) key point detection algorithm was used to train the feature point detection model, and 18 profile feature points in a “measurement unit” were extracted. Secondly, the feature point coordinates are transformed from the image coordinate system to the weld coordinate system, and the weld profiles are fitted by the least squares method. Finally, the measurement system is calibrated with a coplanar linear calibration algorithm to perform pixel distance to actual distance conversion for quantitative detection of geometric dimensions. The experimental results show that the accuracy of the proposed method for key point localization is 95.6%, the mean value of the coefficient of determination R-square for curve fitting is greater than 94%, the absolute error of measurement is between 0.06 and 0.15 mm, and the relative error is between 1.27% and 3.12%. The measurement results are more reliable, and the efficiency is significantly improved compared to manual measurement.