WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld Radiographic Testing

TL;DR

This paper introduces WRT-SAM, a novel foundation model-based segmentation approach for weld radiographic testing that leverages SAM with specialized modules to improve defect detection and generalization across scenarios.

Contribution

The work is the first to adapt SAM for weld radiographic defect segmentation, integrating frequency and multi-scale prompts to enhance performance and generalization.

Findings

WRT-SAM achieves 78.87% recall and 84.04% precision.

The model attains an AUC of 0.9746, setting a new SOTA.

Exhibits strong zero-shot generalization in diverse scenarios.

Abstract

Radiographic testing is a fundamental non-destructive evaluation technique for identifying weld defects and assessing quality in industrial applications due to its high-resolution imaging capabilities. Over the past decade, deep learning techniques have significantly advanced weld defect identification in radiographic images. However, conventional approaches, which rely on training small-scale, task-specific models on single-scenario datasets, exhibit poor cross-scenario generalization. Recently, the Segment Anything Model (SAM), a pre-trained visual foundation model trained on large-scale datasets, has demonstrated exceptional zero-shot generalization capabilities. Fine-tuning SAM with limited domain-specific data has yielded promising results in fields such as medical image segmentation and anomaly detection. To the best of our knowledge, this work is the first to introduce SAM-based segmentation for general weld radiographic testing images. We propose WRT-SAM, a novel weld radiographic defect segmentation model that leverages SAM through an adapter-based integration with a specialized prompt generator architecture. To improve adaptability to grayscale weld radiographic images, we introduce a frequency prompt generator module, which enhances the model's sensitivity to frequency-domain information. Furthermore, to address the multi-scale nature of weld defects, we incorporate a multi-scale prompt generator module, enabling the model to effectively extract and encode defect information across varying scales. Extensive experimental evaluations demonstrate that WRT-SAM achieves a recall of 78.87%, a precision of 84.04%, and an AUC of 0.9746, setting a new state-of-the-art (SOTA) benchmark. Moreover, the model exhibits superior zero-shot generalization performance, highlighting its potential for practical deployment in diverse radiographic testing scenarios.