A flexible deep learning framework for thermographic inspection of composites

Infrared thermography (IRT) is a promising inspection technique, showing good defect detectability in a range of materials. To advance the IRT inspection technique, automated defect detection and analysis are of high interest. This study proposes an object detection algorithm based on Faster R-CNN with attention-based feature fusion network and flexible late fusion strategy for efficient extraction of defect features from an IRT image sequence. In order to train the deep learning-based algorithm, a large, diverse and representative virtual thermographic dataset for composite materials was constructed by an in-house developed parameterized 3D finite element simulator. The virtually trained deep learning framework is tested on experimental IRT datasets which were obtained on composite coupons having a range of artificial defect types as well as on stiffened aerospace composite panels with real (production) defects. The obtained test results indicate the high performance, generalization and reliability of the proposed deep learning framework for automated thermographic inspection of composite parts.

Automated summary

Infrared thermography (IRT) is a promising technique for defect detection in various materials. This study proposes an object detection algorithm based on Faster R-CNN for efficient extraction of defect features from IRT images. A virtual thermographic dataset for composite materials was constructed using a parameterized 3D finite element simulator. The deep learning framework trained on this dataset achieved high performance in the automated thermographic inspection of composite parts.