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arXiv Machine Learning By Mehrdad Shafiei Dizaji, Hoda Azari

A Visual Question Answering Model to Automate Nondestructive Evaluation Image Analysis

Read the original on arXiv Machine Learning →

This article presents a Visual Question Answering (VQA) model tailored for nondestructive evaluation (NDE) image analysis. The system combines a ResNet‑50 image encoder with a GPT‑2 language generator, allowing inspectors to ask targeted questions such as "Is there a crack?" or "Where is the defect located?" and receive precise answers. By facilitating direct question‑and‑answer interactions, the VQA model aims to improve inspection efficiency, reduce errors, and enhance usability in field scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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