## 3 Thermography Data Collection and Preparation  
### Real Thermographic Image Dataset Generation  
To evaluate the defect detection performance of the deep learning model through experimental methods, 24 real specimens with varying depths and sizes of defects, as well as 6 non-defect real specimens, were fabricated to acquire a real thermographic dataset. The specimens were composed of composite materials with a layered structure of CFRP and GFRP, simulating the cross-section of a wind turbine blade. CFRP was manufactured using the pultrusion method, with a longitudinal modulus of 145 GPa. Each GFRP layer has a thickness of 0.91 mm and is stacked in a [0\({}^{\circ}\)/90\({}^{\circ}\)] orientation. The epoxy matrix used was KFR-1258L/KFH-164. The entire specimen was fabricated using the resin infusion process. Table 2 and Figure 2 display the ID of the real specimens, the thickness of CFRP and GFRP, and the depth and size of the defects. Specimens with an ID starting with RN indicate non-defect specimens, while those starting with RD indicate specimens with defects. The number following RD represents the depth (in tenths of a millimeter, 0.1 mm) of the inserted defect, and the last two digits indicate the size of the defect (width \(\times\) height in mm\({}^{2}\)). The thickness of CFRP and GFRP are shown in Figure 2a, and the defects were simulated using Teflon tape with a thickness of 1 mm. The depth of the defect is measured from the heating panel. The defects are square-shaped and consist of five sizes: \((10\,\times\,10)\) mm\({}^{2}\), \((20\,\times\,20)\) mm\({}^{2}\), \((30\,\times\,30)\) mm\({}^{2}\), \((35\,\times\,35)\) mm\({}^{2}\), \((40\,\times\,40)\) mm\({}^{2}\), and \((50\,\times\,50)\) mm\({}^{2}\). Figure 2b shows photographs of specimens from RD42-10 to RD42-50, with the white areas representing the defects inserted inside. The real thermographic data were captured using the FLIR E96 infrared camera on the unheated side with a resolution of \(640\times 480\). The defect specimens in the real thermographic dataset were placed on a heating panel, maintaining a uniform temperature of 40 \({}^{\circ}\)C and heated using a steady-state thermography method until thermal equilibrium was reached. Due to the limited number of non-defect specimens compared to the defect specimens, these were captured under varying temperature conditions (30 \({}^{\circ}\)C, 40 \({}^{\circ}\)C) until thermal equilibrium was achieved. Among the non-defect specimens, RN-1 and RN-4 were filmed until the heating panel reached 50 \({}^{\circ}\)C from room temperature 22 \({}^{\circ}\)C. The thermographic data of RN-1 and RN-4 were used as non-defect class data for training the deep learning model. The average time required for the heating panel and real specimens to reach thermal equilibrium was 300 s. The acquired thermographic data were cropped to focus on the specimen area and resized to a resolution of \(200\times 200\), forming the real thermographic dataset, that is, the real image sequences. Analysis revealed a subtle temperature difference of 0.1 \({}^{\circ}\)C to 0.3 \({}^{\circ}\)C between the defect and non-defect regions. When using this data as input for the deep learning model, a preprocessing method was necessary to enhance visual contrast by emphasizing the pixel values corresponding to the temperature differences. Figure 3 illustrates the preprocessing method for the thermographic data from the real specimens. At time \(t\), the region \(T^{t}\) of the thermographic data captured by the infrared camera, which corresponds to the specimen, is a square matrix composed of temperature values. Among the \(k\) thermographic data consecutively acquired over a certain period, the highest temperature, \(T_{\max}^{tt+k-1}\), and the lowest temperature, \(T_{\min}^{tt+k-1}\), can be defined. The temperature range between \(T_{\max}^{tt+k-1}\) and \(T_{\min}^{tt+k-1}\) is normalized by setting \(T_{\max}^{tt+k-1}\) to 255 and \(T_{\min}^{tt+k-1}\) to 0.