A Visual Question Answering Model to Automate Nondestructive Evaluation Image Analysis

Abstract:This study introduces a Visual Question Answering model designed specifically for nondestructive evaluation applications. VQA models allow inspectors to interactively query NDE images, asking targeted questions like, Is there a crack or Where is the defect located and receive precise answers from the model. Leveraging deep learning and natural language processing, the developed system integrates image feature extraction (via a ResNet-50 model) and language generation capabilities (via GPT-2) to provide accurate, informative feedback. By enabling direct question-and-answer interactions, this VQA model significantly improves inspection efficiency, reduces potential errors, and enhances usability in practical field scenarios.

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2608.29408 [cs.CV]

(or arXiv:2608.29408v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2608.29408 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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