用问答模型自动分析无损检测图像，提升效率与准确性。
A Visual Question Answering Model to Automate Nondestructive Evaluation Image Analysis
- 结合ResNet-50与GPT-2，实现图像理解与自然语言回答。
- 支持交互式提问，可精准定位缺陷位置或判断是否存在裂纹。
- 适用于现场检测场景，减少人工误判，提升操作便利性。
本研究提出一种专用于无损检测（NDE）应用的视觉问答（VQA）模型。该模型使检测人员可通过交互式提问，如‘是否存在裂纹’或‘缺陷位于何处’，从模型获取精准答案。系统融合基于ResNet-50的图像特征提取与基于GPT-2的语言生成能力，实现对图像内容的准确理解与信息反馈。通过直接问答交互，该模型显著提升检测效率，降低潜在错误，并增强在实际现场环境中的可用性。
原文摘要 · Abstract (English)
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.
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