# Active Thermography for the Detection of Sub-Surface Defects on a Curved and Coated GFRP-Structure
## 3 Experimental Set-Up
### Image Processing  
In the next step, an image processing, which is implemented in Python, is applied to the acquired thermogram sequences to extract the contrast information for the defect visualization as well as to detect the defects; see Figure 5. For the image processing, the first 15 s (300 thermograms) of the total recording are used, since the largest temperature gradients are found at the beginning of the cooling phase. Furthermore, only the ROIs shown in Figure 2 are considered in order to reduce artefacts and edge effects as well as to keep the computational effort and the processing time low. Furthermore, each image is normalized for the purpose of comparability. The largest intensity value of the image is assigned to the value 1, the smallest intensity value of the image is assigned to the value 0. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Test Series** & **1** & **2** & **3** \\ \hline ROI & A & B & A \\ excitation time \(t_{\text{exc}}\) [s] & 10, 15, 20, 25, 30 & 15, 20, 25, 30, 35 & 25 \\ distance \(k\) [mm] & 22, 43, 60, 89, 114, 155, 300 & 60 \\ angular perspective \(\alpha\), \(\beta\) [\({}^{\circ}\)] & 0 & 25, 45 \\ excitation power [kW] & 1 & \\ recording time \(t_{\text{rec}}\) [s] & 30 & \\ rec. frequency [Hz] & 20 & \\ integration time [\(\upmu\)s] & 1600 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of the parameters of every test series, compared with the measurement arrangements shown in Figures 3 and 4. Figure 4: Schematic representation of the angular perspective with different offsets between the defect and the optical axis of the camera, \(\alpha=25^{\circ}\) and \(\beta=45^{\circ}\). Figure 3: Photograph of the developed test set-up with the distance \(k=114\,\text{mm}\) between the excitation unit and the test specimen. As pre-processing methods, both the selection of the highest contrast single picture or a PCA are available. The algorithm for the highest contrast single picture selects one thermogram from the 300 thermograms that has the largest intensity difference between a defective and a non-defective area and thus the highest contrast. Mostly, the highest contrast is found in one of the first 10 pictures of the recording series, where the temperature at the defective area is significantly higher than at the non-defective area. With PCA, the output image is extracted from all thermograms; see Section 2.2. Here, the highest contrast single picture serves as a reference method to demonstrate the benefit of the PCA concerning information extraction and contrast enhancement. For the filtering of the image and thus contrast enhancement, the noise in the resulting images of the pre-processing must be reduced. Therefore the images are filtered two times with a 16 th order binomial filter acting as a low-pass filter. An additional contrast enhancement is achieved by removing the background. In this process, the minima located on a line between the defects, represented as image maxima, are found at first. Assuming that these minima correspond approximately to the image background, a threshold is set and the minima that lie beneath the threshold are eliminated. By interpolating between the different remaining image minima, a synthetic background is generated and subtracted from the image to be processed. To smoothen the resulting image, the same filter as before is applied again one time. This completes the filtering of the image. In a final step, automatic defect detection is applied. Here, the detection algorithm searches for image maxima located at the same pixel of both an image column and an image row. These maxima are now detected as potential defects (feature extraction) and highlighted with a red circle. In order to clarify the contrast enhancement of the PCA and to be able to make quantitative statements about the defect visibility, the CNR of each potential defect in all resulting images (with and without filtering) is calculated; see Section 2.2.