摘要
The presence of internal defects poses a serious challenge to the structural integrity and performance of composite materials such as polymers and cultural heritage. Therefore the application of non-destructive testing (NDT) techniques is essential. Active infrared thermography (AIRT) is becoming increasingly attractive among many NDT techniques due to its low - cost and wide - area coverage advantages. However, thermograms often involve non - uniform backgrounds and measurement noise caused by uneven heating and environmental reflections, necessitating post-processing procedures. Among the research topics in this area, unsupervised machine learning methods have shown promising success in AIRT for defect detection. This paper aims to provide a recent overview of unsupervised machine learning-aided thermography for defect detection. Six perspectives on the role of machine learning in thermogram sequence processing are presented: image denoising, non-uniform background removal, image super-resolution enhancement, feature extraction, image segmentation, and depth prediction. In particular, deep learning methods for thermographic data analysis are reviewed and emphasised. A step-by-step review of treatment options offers a guide for inexperienced readers and investigators entering the field. Additionally, the development of machine learning-based thermography methods for different scenarios is summarised from an application perspective. Finally, an outlook on the prospects and potential of these methods is provided.