Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.10549v1</id>\n    <title>Towards Cognitive Defect Analysis in Active Infrared Thermography with Vision-Text Cues</title>\n    <updated>2026-03-11T08:58:15Z</updated>\n    <link href='https://arxiv.org/abs/2603.10549v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.10549v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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%.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='eess.SP'/>\n    <published>2026-03-11T08:58:15Z</published>\n    <arxiv:primary_category term='cs.CV'/>\n    <author>\n      <name>Mohammed Salah</name>\n    </author>\n    <author>\n      <name>Eman Ouda</name>\n    </author>\n    <author>\n      <name>Giuseppe Dell'Avvocato</name>\n    </author>\n    <author>\n      <name>Fabrizio Sarasini</name>\n    </author>\n    <author>\n      <name>Ester D'Accardi</name>\n    </author>\n    <author>\n      <name>Jorge Dias</name>\n    </author>\n    <author>\n      <name>Davor Svetinovic</name>\n    </author>\n    <author>\n      <name>Stefano Sfarra</name>\n    </author>\n    <author>\n      <name>Yusra Abdulrahman</name>\n    </author>\n  </entry>"
}