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

Mohammed Salah, Eman Ouda, Giuseppe Dell'Avvocato, Fabrizio Sarasini, Ester D'Accardi, Jorge Dias, Davor Svetinovic, Stefano Sfarra, Yusra Abdulrahman

TL;DR

A novel language-guided framework for cognitive defect analysis in CFRPs using AIRT and vision-language models (VLMs) relies solely on pretrained multimodal VLM encoders coupled with a lightweight adapter to enable generative zero-shot understanding and localization of subsurface defects.

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%.

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

TL;DR

A novel language-guided framework for cognitive defect analysis in CFRPs using AIRT and vision-language models (VLMs) relies solely on pretrained multimodal VLM encoders coupled with a lightweight adapter to enable generative zero-shot understanding and localization of subsurface defects.

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%.
Paper Structure (16 sections, 15 equations, 6 figures, 5 tables)

This paper contains 16 sections, 15 equations, 6 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Materials and Methods
  4. Specimens and Data Collection
  5. Specimens
  6. Data Collection
  7. Methodology
  8. AIRT-VLM Adapter
  9. Cognitive Defect Analysis
  10. Experiments
  11. Experimental Evaluation
  12. AIRT-VLM Adapter Evaluation
  13. Defect Detection Evaluation
  14. Ablation Studies
  15. Limitations
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Front-side view of the impacted specimens, subjected to low-velocity impact at 5 J and 15 J.
  • Figure 2: Top: Front-side heating with pulsed flash lamps (A.1-A.2) and front-side long-pulse halogen heating (A.3). Bottom: Schematic representation of inspection setup.
  • Figure 3: Top: Backside heating with flash (B.1) and halogen lamp (B.2). Bottom: Schematic representation of transmission inspection setup.
  • Figure 4: Overview of the defect analysis framework. The inspection sequence is preprocessed to generate a compact single image representation similar to the VLMs pretraining domain. Consequently, a VLM generates bounding boxes for defects.
  • Figure 5: Aggregate a) contrast and b) SNR on all 25 CFRP inspection sequences.
  • ...and 1 more figures