摘要
Infrared thermography for quality assessment of polymer composites has gained increasing attention with the development of various thermographic data analysis methods. However, low-quality thermal images containing noise and inhomogeneous backgrounds restrict the defect detection performance of these methods. In this work, a convolutional graph thermography (CGT) method is proposed for subsurface defect detection in polymer composites. With a developed convolutional autoencoder thermogram enhancer, the noise and inhomogeneous background of the thermal image can be reduced. Subsequently, a graph-based dimensionality reduction method performs feature extraction on the enhanced thermograms. Consequently, the low-dimensional intrinsic manifolds of the high-dimensional thermographic data are characterized to highlight the defects visually. Two case studies on carbon fiber reinforced polymers demonstrated the performance of the proposed method.