Towards Cognitive Defect Analysis in Active Infrared Thermography with Vision-Text Cues

arXiv cs.CV / 3/12/2026

📰 NewsModels & Research

共有:

Key Points

It proposes a novel language-guided framework for cognitive defect analysis in CFRP using active infrared thermography and vision-language models, enabling zero-shot defect understanding and localization without large training datasets.
It introduces an AIRT-VLM Adapter that aligns thermographic data with pretrained multimodal encoders to enhance defect visibility and reduce domain gaps.
Validation on 25 CFRP inspection sequences with defects at different energy levels shows SNR gains exceeding 10 dB compared with traditional dimensionality-reduction methods and zero-shot defect localization with IoU up to 70%.
The study evaluates three VLMs—GroundingDINO, Qwen-VL-Chat, and CogVLM—demonstrating cross-model applicability and potential for scalable AI-driven NDE in industry.

Abstract

Active infrared thermography (AIRT) is currently witnessing a surge of artificial intelligence (AI) methodologies being deployed for automated subsurface defect analysis of high performance carbon fiber-reinforced polymers (CFRP). Deploying AI-based AIRT methodologies for inspecting CFRPs requires the creation of time consuming and expensive datasets of CFRP inspection sequences to train neural networks. To address this challenge, this work introduces a novel language-guided framework for cognitive defect analysis in CFRPs using AIRT and vision-language models (VLMs). Unlike conventional learning-based approaches, the proposed framework does not require developing training datasets for extensive training of defect detectors, instead it relies solely on pretrained multimodal VLM encoders coupled with a lightweight adapter to enable generative zero-shot understanding and localization of subsurface defects. By leveraging pretrained multimodal encoders, the proposed system enables generative zero-shot understanding of thermographic patterns and automatic detection of subsurface defects. Given the domain gap between thermographic data and natural images used to train VLMs, an AIRT-VLM Adapter is proposed to enhance the visibility of defects while aligning the thermographic domain with the learned representations of VLMs. The proposed framework is validated using three representative VLMs; specifically, GroundingDINO, Qwen-VL-Chat, and CogVLM. Validation is performed on 25 CFRP inspection sequences with impacts introduced at different energy levels, reflecting realistic defects encountered in industrial scenarios. Experimental results demonstrate that the AIRT-VLM adapter achieves signal-to-noise ratio (SNR) gains exceeding 10 dB compared with conventional thermographic dimensionality-reduction methods, while enabling zero-shot defect detection with intersection-over-union values reaching 70%.