## 1 Introduction
In addition, deep learning tools such as \"Data Augmentation\" and \"Transfer Learning\" are used to facilitate the iterative learning of the algorithm. [PERSON] et al. combined thermography tests with deep learning techniques in order to detect cracks [12]. [PERSON] et al. used convolutional neural networks (CNN) to distinguish phases from shape and edge information rather than intensity differences and successfully segment phases in a unidirectional composite that also had a coating with similar image intensity [13]. In fact, such algorithms are also suitable for defect detection. These neural networks use folding layers to detect the different features of an image, such as edges, corners, and lines. The open-source nature of deep learning offers an ever-growing online community that allows access to established neural networks such as U-Net [14]. Since rain erosion on wind turbine blades has to be investigated in the open field at the turbine itself, there is a need for a measurement technique for in-situ inspections. Active thermography is a non-destructive measurement method for detecting defects close to the surface. Unlike CT and XRM, it can be used in the open field for in-situ measurements. Previous studies have already shown good suitability for defect detection in rotor blade leading edge-like specimens, where the defects were simulated by drilling holes from the back side of the specimens [15]. The extent to which realistic defects can be detected, such as trapped air bubbles, which have different properties than inserted borings, has not yet been investigated. With regard to the visualization and observation of damage growth, several thermographic investigations have already been successfully carried out on partially pre-damaged test specimens made of fiber-reinforced composites with different loads. [PERSON] et al. performed thermographic studies on GFRP plates pre-damaged with delaminations and observed the damage growth under tensile loading [16]. Thermographic imaging of impact damage to GFRP plates caused by impacts with different hammers in a drop-weight testing machine was performed by [PERSON] et al. [17]. [PERSON] al. also thermographically investigated the impact damage on GFRP sheets, which were pre-damaged by production defects such as porosity during hand lamination prior to impact loading [18]. All studies show that thermography can be used to visualize the condition of a pre-damaged or undamaged fiber-reinforced composite plate before and after different impacts and to document the damage growth that occurred. However, the detection capabilities of thermography with respect to damage growth beginning with a realistic sub-surface defect in a rotor-blade-leading-edge-like test specimen up to the surface damage--as it occurs on wind turbines in operation due to the exposure to rain--need to be identified. This work aims to clarify whether damage that is close to reality can be produced in the laboratory and whether it induces surface damage under a rain load. It will also address the lack of correlation between the condition before and after erosion in order to enable the prevention and early detection of incipient erosion. To achieve this goal, defined initial defects are introduced into rotor-blade-leading-edge-like test specimens, which are then subjected to loading in a rain erosion test facility. The test specimens are analyzed in the initial state and after the rain loading using XRM, CT, and an imaging surface analytical method. Thermographic investigations are carried out as a feasibility study for in-situ measurements at the rotor blade leading edge. In addition, the resolution of the current damage state will be clarified and, thus, the visualization, detection, and differentiation of the defects below and on the surface of rotor-blade-leading-edge-like specimens after rain exposure.