Research on welding seam extraction based on PCA-DBSCAN

Aiming at the problem that the welding seam extraction after welding has a large error and is not easy to extract, the article proposes a welding seam extraction algorithm that is a fusion of DBSCAN clustering (density-based spatial clustering of applications with noise) and the improved principal component analysis (PCA) algorithm. Firstly, the weld image is pre-processed with grey scale and adaptive median filtering; secondly, the Canny edge detection algorithm is applied to the image to extract the weld edges, and the weld edges are clustered using the DBSCAN density clustering algorithm; after that, the principal components of the weld are searched for based on the improved PCA algorithm, and the weld is mapped to the principal vectors for statistical mapping, and the left and right boundaries of the weld are obtained by automatically assigning a threshold value according to the image resolution. The left and right boundaries of the weld are then reflected to the secondary principal vectors to obtain the upper and lower boundaries of the weld; finally, three groups of comparison experiments were completed according to the method of this paper, which analysed the influence of the algorithm of this paper by the resolution, welding method, light intensity and other factors. The experiment proves that the algorithm of this paper has good effect on straight seam extraction, and the extraction accuracy is more than 95%.