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

arXiv: https://arxiv.org/abs/2502.11338 · HTML: https://mlsanity.com/paper/2502.11338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation](https://mlsanity.com/paper/2209.08575/) (2022)
- [MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model](https://mlsanity.com/paper/2211.00611/) (2022)
- [FcaNet: Frequency Channel Attention Networks](https://mlsanity.com/paper/2012.11879/) (2020)
- [Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models](https://mlsanity.com/paper/2310.06313/) (2023)
- [IMAGDressing-v1: Customizable Virtual Dressing](https://mlsanity.com/paper/2407.12705/) (2024)
- [Boosting Consistency in Story Visualization with Rich-Contextual Conditional Diffusion Models](https://mlsanity.com/paper/2407.02482/) (2024)
- [RobustSAM: Segment Anything Robustly on Degraded Images](https://mlsanity.com/paper/2406.09627/) (2024)
- [Visual Tuning](https://mlsanity.com/paper/2305.06061/) (2023)
- [PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation](https://mlsanity.com/paper/2401.13051/) (2024)

## Abstract (arXiv)

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.