Parameter Self-Optimizing Clustering for Autonomous Extraction of the Weld Seam Based on Orientation Saliency in Robotic MAG Welding

This paper presents an effective method which needs free parameters as little as possible to autonomously extract the weld seam profile and edges from the molten background in two kinds of weld images within robotic MAG welding. First, orientation saliency detection produced by Gabor filtering nicely highlights the weld seam profile and edges from the molten background. Then, an unsupervised clustering algorithm combing a cluster validity index via an optimization rule, referred to as parameter self-optimizing clustering, is applied to discern the weld seam profile and edges from interference data after the orientation saliency detection result is given threshold segmentation. The validity index is better than the classical ones in two kinds of data sets through considerable tests. Last, two common applications of weld seam identification demonstrate the effectiveness of the proposed method.

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