A flexible deep learning framework for thermographic inspection of composites

Tong, Zongfei; Cheng, Liangliang; Xie, Shejuan; Kersemans, Mathias

doi:10.1016/j.ndteint.2023.102926

NDT & E International

2023

DOI: 10.1016/j.ndteint.2023.102926

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A flexible deep learning framework for thermographic inspection of composites

Zongfei Tong

Liangliang Cheng

Shejuan Xie

et al.

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Cited by 7 publications

References 37 publications

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Energy-efficient buildings with energy-efficient optimized models: a case study on thermal bridge detection

Fişne,

Yurtsever,

Eken

2024

Cluster Comput

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Thermographic inspection is particularly effective in identifying thermal bridges because it visualizes temperature differences on the building’s surface. The focus of this work is on energy-efficient computing for deep learning-based thermal bridge (anomaly) detection models. In this study, we concentrate on object detection-based models such as Mask R-CNN_FPN_50, Swin-T Transformer, and FSAF. We do benchmark tests on TBRR dataset with varying input sizes. To overcome the energy-efficient design, we apply optimizations such as compression, latency reduction, and pruning to these models. After our proposed improvements, the inference of the anomaly detection model, Mask R-CNN_FPN_50 with compression technique, is approximately 7.5% faster than the original. Also, more acceleration is observed in all models with increasing input size. Another criterion we focus on is total energy gain for optimized models. Swin-T transformer has the most inference energy gains for all input sizes ($$\approx$$ ≈ 27 J for 3000 x 4000 and $$\approx$$ ≈ 14 J for 2400 x 3400). In conclusion, our study presents an optimization of size, weight, and power for vision-based anomaly detection for buildings.

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Energy-efficient buildings with energy-efficient optimized models: a case study on thermal bridge detection

Fişne,

Yurtsever,

Eken

2024

Cluster Comput

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Applications of artificial intelligence/machine learning to high-performance composites

Wang,

Zhang

2024

Composites Part B: Engineering

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AI-based optical-thermal video data fusion for near real-time blade segmentation in normal wind turbine operation

Jia,

Chen

2024

Engineering Applications of Artificial Intelligence

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A flexible deep learning framework for thermographic inspection of composites

Cited by 7 publications

References 37 publications

Energy-efficient buildings with energy-efficient optimized models: a case study on thermal bridge detection

Energy-efficient buildings with energy-efficient optimized models: a case study on thermal bridge detection

Applications of artificial intelligence/machine learning to high-performance composites

AI-based optical-thermal video data fusion for near real-time blade segmentation in normal wind turbine operation

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