# A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks
## 1 Introduce  
Active infrared thermography (AIRT) is widely used in material defect detection by virtue of its visualization and accuracy [1; 2]. When the test specimen is subjected to thermal excitation, the irregular inner structure leads to an uneven distribution of the surface temperature field. This phenomenon makes the defect depth determined by AIRT possible. In a study by [PERSON] et al., they heated one side of aerospace composites to detect subsurface defects using AIRT [3]. Several non-destructive evaluation (NDE) methods have been applied to deal with thermographic sequences, such as independent component thermography (ICT) [4], pulse phase thermography (PPT) [5], thermographic signal reconstruction (TSR) [6], as well as principal component thermography (PCT) [7; 8; 9]. In order to eliminate redundant information and reduce computational cost, PCA is adopted in this work to process original temperature datasets.  
Quantitative evaluation is a research focus in the field of NDE. Defect information can be detected by peak temperature, peak time, and temperature information at a single moment [10]. Another way to approach defect evaluation is to use deep learning. While it is being widely used in signal processing [11], intelligent control [12], and pattern recognition [13], deep learning also plays an important role in the classification and depth recognition of defect [14; 15]. [PERSON] et al. used the finite element method (FEM) and neural networks to evaluate defect depth [16]. [PERSON] et al. proposed a strategy based on long short-term memory (LSTM) networks for the classification of defects [17]. The results demonstrated that deep learning is capable of defect quantitative evaluation. Gated recurrent unit (GRU) is particularly designed for processing temporal sequences to overcome gradient disappearance and explosion in the training and testing process of recurrentneural networks (RNN) [18; 19]. Compared with LSTM networks, the GRU networks utilize only a few learning parameters, which improves computational efficiency and can also receive appreciable performance.  
In this work, we focus on the defect depth recognition of flat bottom hole defects in polymethyl methacrylate (PMMA) via GRU networks. Halogen lamps are used to heat the PMMA specimen. The thermal response (temperature values change) in the heating and cooling periods is obtained and used as data for the raw temperature datasets. The PCA method is exploited to reconstruct the raw temperature datasets as another principal component datasets. The raw temperature datasets and principal component datasets are used to train and test the GRU model, respectively. The results show that the accuracy of the GRU model trained by principal component datasets is better in defect depth recognition than the model trained by raw temperature datasets. Compared with traditional networks, the GRU model is appealing in processing the thermal sequence.  
The rest of this paper is organized as follow: Section 2 describes the theory of active infrared thermography. Section 3 presents the framework of the proposed defect depth recognition method based on the GRU model. Section 4 illustrates the experiment specimen and system. Section 5 provides the results and analysis. Finally, conclusions are given in Section 6.