## 5 Conclusions and Outlook  
An image processing method for localizing the laminar-turbulent flow transition line in thermographic images of wind turbine rotor blades in operation which is sensitive with respect to premature flow transitions was introduced. The transition line is located by evaluating local gradient maxima in the thermographic image. To achieve this, two gradient evaluation methods were introduced that use the a priori knowledge about the orientation of flow transition line segments. The first method uses this knowledge to analyze a 2D gradient field with respect to the magnitude and the direction of the gradients. The second method changes the alignment of a 1D gradient evaluation \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Method A** & **Method B** \\ \hline \(\overline{\text{LFR}}=24\%\) & 20\% & 22\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Calculated laminar flow reduction compared between the manual transition line evaluation and the two introduced image processing methods. before applying a magnitude threshold. This way, both methods are able to maximize the sensitivity of the evaluation with respect to locate local gradient maxima representing premature flow transitions. Both methods were verified on simulated images based on real measurements and proved to decrease the mean euclidean distance between the evaluated and true transition line location along the rotor blade surface. Additionally, a study of different contrasts between the wedge shaped premature transition regions with turbulent flow and the surrounding laminar flow regime, different magnitudes of image noise and the overlapping of premature transition regions were conducted. Both introduced methods proved to be robust towards the detection of premature flow transitions and are able to cope with lower contrasts and higher image noise compared to the state-of-the-art image processing. The measurement uncertainty of the localization was reduced by both introduced methods if a minimum contrast of the turbulence wedge is present. Afterwards, a real thermographic image was used in order to validate the introduced image processing method. A visual inspection of the results showed that the number of detected turbulence wedges was increased to 11 turbulence wedges. As a comparison, the state-of-the-art image processing could not detect any of the turbulence wedges sufficiently. This allowed for a more accurate quantification of the laminar flow reduction (LFR) as the deviation of the evaluated flow transition line was reduced. Due to non-existing local gradients at the position of premature flow transition near the leading edge of the rotor blade, the tips of turbulence wedges were not detected successfully by either image processing method. To improve the flow transition line localization, an extrapolation of the successful located turbulence wedges' flanks could be conducted in order to close the wedge at their tips. Another possibility would be the fitting of wedge shapes form into located premature flow transitions. A detection of the premature flow transitions in regions without locale gradient maxima to be evaluated would further decrease the measurement uncertainty of the flow transition line localization. The improved flow transition line localization enables more accurate estimation of the annual energy production loss as the laminar flow reduction due to surface defects can be quantified with reduced uncertainties. The image processing used in previous work focusing on this estimation is equal to the method this work denotes as state-of-the-art [27]. Consequently, by using the introduced methods, the transition line localization takes multiple turbulence wedges with low contrast into account that were excluded with the state-of-the-art method, thus improving the estimation of the annual energy production loss. As a result, the impact of the environment on the rotor blade surface condition and consequently the boundary layer flow can be analyzed more accurately.