Automatic and reliable anomaly detection without human intervention is a challenging task in thermal wave imaging. For this purpose, various processing algorithms proposed earlier are outperforming one another to exhibit smaller and deeper anomalies. Machine learning-based defect detection approaches are attracting the research community due to their reliable performance on employing over the stimulated thermal response in active thermography. The present article explores the deployment of local outlier factor (LOF), one-class support vector machine (OCSVM), and isolation forest (IF) algorithms over an experimental carbon fiber reinforced polymer specimen with artificially drilled flat bottom holes of different sizes at different depths to verify the detection capability of these approaches in Quadratic frequency modulated thermal wave imaging even with noisy synthetic data. In addition, a quantitative study has been performed using various thermographic and machine learning based performance metrics.

在没有人为干预的情况下，自动可靠的异常检测是热波成像中的一项艰巨任务。为此，较早提出的各种处理算法彼此之间表现出越来越小的异常表现。基于机器学习的缺陷检测方法因其在主动热成像中采用受激热响应的可靠性能而吸引了研究界的注意。本文探讨了局部离群因子（LOF），一类支持向量机（OCSVM）的部署，和隔离森林（IF）算法在具有不同大小，不同深度的不同大小的人工钻制的平底孔的碳纤维增强聚合物实验样品上进行验证，以验证这些方法在二次频率调制热波成像中的检测能力，即使有嘈杂的合成数据也是如此。此外，已经使用各种基于热成像和机器学习的性能指标进行了定量研究。