# A Dataset of Pulsed Thermography for Automated Defect Depth Estimation
## 1 Introduction  
The critical role of Nondestructive-Evaluation (NDE)-based quality assurance within modern society cannot be overstated. The NDE technique allows for the assessment of components without causing any damage, making it indispensable in a wide range of industries, including aerospace [1; 2; 3] and civil engineering [4; 5]. Over the past century, a number of NDE methods have been developed, such as ultrasound [2; 6; 7], eddy current [8; 9], guided wave [10], and infrared thermography [11], among others. Among these methods, Infrared Thermography (IRT) has gained widespread popularity for inspecting industrial components. Compared to other methods such as Computed Tomography (CT) or ultrasound, IRT offers a swift and cost-effective solution [11]. In practice, IRT experiments utilize an infrared camera to detect and record the transient thermal patterns on the surface of the testing specimen, with which valuable diagnostic information can be obtained. An important variant of IRT experimentation is Pulsed Thermography (PT). In PT experiments, the testing specimen is exposed to a heat source, such as a high-power lamp, resulting in surface heating. Subsequently, the thermal wave propagates into the subsurface of the component where the potential defects could appear. This method exploits the distinct thermal properties and characteristics of sound areas versus defective areas, resulting in disparate thermal behaviors. As a result, PT effectively distinguishes defects from sound regions of the test component. PT finds its greatest utility in inspecting materials characterized by relatively high thermal conductivity, including metals, Carbon-Fiber-Reinforced Plastic (CFRP), Glass-Fiber-Reinforced Plastics (GFRP), or Polyvinyl Chloride (PVC) materials. In recent years, significant efforts have been dedicated to applying Artificial Intelligence (AI) techniques to the NDE data analysis. This includes the characterization of defects within ultrasound data [12; 13], as well as advancements in CT data processing [14; 15]. For IRT data analysis, current research primarily targets defect detection and the improvement of infrared imagery quality [16; 17]. However, it is worth noting the significance of defect characterization in various industrial contexts. In particular, the depth estimation of defects is crucial for repair planning. Nonetheless, a major impediment to this advancement for IRT testing is the lack of publicly accessible datasets suitable for training deep learning models. Furthermore, accurately labeling of the 3D spatial location of defects poses a substantial challenge, as these anomalies are typically concealed beneath the surface of the test specimen. In our previous work, a thermographic dataset for defect detection using deep learning methods was proposed [18]. However, this initial effort was limited by its focus on a single defect shape, reliance on Principal Component Thermography (PCT) images for labeling, and the absence of defect depth information. Building on this foundation, this study introduces a dataset with significant enhancements, including detailed defect depth information and refined labeling techniques, to further advance the field. Besides, to ensure accurate ground truth data, two distinct data labeling methodologies were implemented: a conventional manual approach and a semi-automated technique. Furthermore, we utilized the YOLOv5 deep learning model to train for the identification of defect locations and their depths. The contributions of this study are threefold. First, a publicly accessible PT dataset specifically designed for defect detection and depth estimation is presented. This dataset, comprising 38 PT measurements, includes defects in two distinct shapes: rectangles and rounds, offering a standardized benchmark for testing various relevant algorithms. Second, a novel labeling methodology based on 3D-CAD files, subjecting it to qualitative and quantitative evaluation, is introduced, which showed a marked improvement over traditional manual labeling.