An Integrated Vision-and-Language Pretraining (VLP) and Visual Question Answering (VQA) model to Automate Nondestructive Evaluation Image Analysis

תקציר מקורי באנגליתarXiv:2608.29408v2 Announce Type: replace-cross Abstract: An AI-based approach called ChatNDE Figure to Caption is introduced, which aims to automate the interpretation of NDE images using deep learning and natural language processing (NLP). A Vision-and-Language Pretraining (VLP) strategy is developed to help the model learn how to connect visual features with meaningful language. Basically, we built a large NDE image dataset, trained the model using annotated examples, and then evaluated how well it performed using BLEU scores to compare its output to expert written descriptions. So, the system combines a ResNet50 model to extract important features from the images and a GPT2 language model to turn those features into natural sounding text. Even though the accuracy of the model has been