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Schwarz, Lea
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document
Unsupervised Segmentation of Industrial X-ray Computed Tomography Data with the Segment Anything Model
Abstract
<jats:title>Abstract</jats:title><jats:p>Industrial X-ray computed tomography (XCT) is a crucial non-destructive testing method for quality control in various branches of industry. Accurate segmentation of XCT data aids in defect identification and material characterization. In this paper we present our results of applying the Segment Anything Model (SAM) in this context. SAM, an unsupervised approach that automates segmentation without manual annotations, combines deep convolutional neural networks and generative adversarial networks. We used SAM on diverse industrial XCT data, and our evaluation demonstrates SAM’s competitive segmentation accuracy and efficiency. SAM’s unsupervised nature and adaptability offer cost-effective and reliable quality assurance solutions in industrial settings, enhancing product quality and inspection processes.</jats:p>