## 5 Conclusions and Outlook  
A model-based image processing algorithm for the detection of turbulence wedges on rotor blades of wind turbines in operation was introduced. The algorithm utilizes an image-processing approach called template matching, a technique for finding parts of a thermogram which match a template image. The templates are wedge-shaped, which imitates the natural shape of a turbulence wedge. Using the template-matching method, turbulence wedges are detected as a whole, including position and size parameters, instead of identifying single positions of the premature transition line, which is the case in the state-of-the-art gradient-based method. Therefore, the approach simplifies the determination \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Area Type** & **Total Wedge Area \(A\)** & \(F_{A}\) **in \%** & \(N\) \\ \hline Reference Value & 2080.0 & 0 & 9 \\ GBA & 1972.9 & \(-5.1\) & 6 \\ MBA & 1659.0 & \(-20.2\) & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Areas determined by the model-based algorithm (MBA) as well as the state-of-the-art, gradient-based algorithm (GBA) for all turbulence wedges in Figure 13. The area values are given in unit pixel\({}^{2}\). Figure 14: Thermogram of rotor blade of an in-service wind turbine with \(N=9\) turbulence wedges. (**a**) shows the preprocessed thermogram with the turbulence wedges numbered with roman numerals. (**b**) shows the reference transition line, which was estimated manually, as well as the CNR of the wedges noted above the respective wedge. (**c**) shows the result of applying the gradient-based algorithm (GBA) of [PERSON] et al. [14] to the image and (**d**) shows the result of the application of the model-based algorithm (MBA) on the thermogram. Note the reduced color scale, which makes the turbulence wedges more visible. of the additional turbulent area on the rotor blade due to turbulence wedges, once the turbulence wedges are identified and located. The positions of the turbulence wedges between the natural transition line and the leading edge are detected first using a cross-correlation of the wedge templates and the thermogram. Then, the areas are determined by comparing the detected wedges to wedge templates of different sizes, where the size of the template with the highest similarity measure is finally considered as the measured size of the turbulence wedge. The model-based algorithm was verified and compared to the state-of-the-art algorithm by evaluating a simulated thermogram with three turbulence wedges of different CNR values. The state-of-the-art algorithm was only able to detect one of the three wedges, leading to a relative deviation of the area of \(-38.5\%\). The area of the single detected wedge was overestimated, which is why the relative deviation is not smaller. On the other hand, the model-based algorithm found all three and also estimated their areas with a relative deviation of \(1.5\%\). A detailed characterization of the algorithm was performed at different CNR values and different wedge sizes. For \(4\leq\text{CNR}\leq 16\), which are CNR values commonly found in real thermograms, the absolute deviation of the determined position stayed below \(2.5\%\) of the average wedge width. The relative deviation of the wedge areas remains below \(10\%\) for \(4\leq\text{CNR}\leq 16\). Overall, wedges with a CNR larger than \(2\) were detectable using the model-based algorithm. To finally validate the model-based algorithm, it was applied to two real thermograms of rotor blades of wind turbines in operation. A comparison with the state-of-the-art algorithm shows an improvement in the number of wedges detected: the state-of-the-art algorithm consistently found fewer wedges than truly present in the thermogram, leading to relative deviations of the area of up to \(-64.2\%\). However, the area of the detected wedges was often overestimated. The model-based algorithm found all wedges but in general underestimated their areas.