A Visual Question Answering Model to Automate Nondestructive Evaluation Image Analysis
Computer Vision and Pattern RecognitionMachine Learning
Summary
The authors developed a Visual Question Answering (VQA) model for nondestructive evaluation, which helps inspectors ask specific questions about inspection images and get accurate answers. Their system combines image analysis using a ResNet-50 neural network and language processing with GPT-2 to understand and respond effectively. This approach aims to make inspections faster, reduce mistakes, and be easier to use in real-world settings.
Authors
Mehrdad Shafiei Dizaji, Hoda Azari
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.