Automatic Segmentation by the Method of Interval Fusion with Preference Aggregation When Recognizing Weld Defects

Abstract

Quality control in welding is usually carried out during the visual inspection process and is highly dependent on an operator experience. In this paper, an approach to automatic detection and classification of a defective region is proposed, in which the segmentation of the analyzed photographic image of a weld (i.e., its division into defective and defect-free regions) is performed using the region growing procedure. The starting points for this procedure are selected by the authors’ robust method of interval fusion with preference aggregation (IF&PA) on the base of image histogram analysis. Testing the proposed approach for real life photographic images showed its ability to detect different types of weld defects with higher accuracy compared to traditional methods, such as the Otsu method and k-means.