## 4 Results
### Validation: Application on Thermograms of an In-Service Wind Turbine  
To validate the MBA, it was applied to real thermograms of rotor blades of in-service wind turbines. In Figure 13a, such a real thermogram of a wind turbine blade with five well-separated turbulence wedges with a CNR value of 6 or more is shown. Since the ground truth is not known, the measured areas can only be compared with a manually created reference, which is depicted in Figure 13b. The angle of the turbulence wedges is specified as \(5^{\circ}\). The state-of-the-art results with the GBA are shown in Figure 13c for comparison; the results of the novel MBA are shown in Figure 13d. The GBA only detects the turbulence wedges with CNR \(>15\), resulting in only two detected turbulence wedges, while the MBA detects all five turbulence wedges, regardless of the CNR value. In Table 3, the values of the determined area, the reference area and the relative deviation of the total  
Figure 12: Analysis of the deviation of the height \(h\), the width \(w\) and the area \(A\) of one wedge in simulated thermogram, averaged over \(n=50\) trials for each relative height value \(\tilde{h}/h_{\text{max}}\). The CNR stays constant at a value of 11. **(a)** shows the mean relative deviation of the number of detected wedges \(\langle F_{N}\rangle\) over the relative true wedge heights \(\tilde{h}/h_{\text{max}}\). The mean normalized position error \(\langle\frac{\Delta x}{\tilde{h}}\rangle\) over the true relative heights \(\tilde{h}/h_{\text{max}}\) of the wedges is shown in **(b)**. The relative deviations of the height, the width and the area are shown in **(c)**. The grey region indicates relative heights commonly found in real thermograms, i.e., \(0.73\pm 1\,\sigma\), with \(1\,\sigma=0.12\). The error bar indicates the standard deviation of the mean. area \(F_{A}\) are displayed. Note that the deviation of the total area for the GBA results in \(F_{A}=-64.2\%\), while the MBA only has a deviation of \(F_{A}=-7.7\%\). The deviation value is in agreement with the deviation determined in the characterization of the algorithm in Section 4.2, where for CNR \(\geq 15\), a deviation of \(F_{A}\leq 8\%\) is expected, see Figure 11c. In contrast to Figure 13, Figure 14a shows a wind turbine blade with nine smaller turbulence wedges that are close together or even touching. The skew of the turbulence wedges is also larger, so that the angle of the turbulence wedges amounts to \(12^{\circ}\). Furthermore, all turbulence wedges have a CNR value below 7. Between the seventh and eighth wedges, a small region with lower intensity might be visible that has a CNR of only \(\text{CNR}=0.2\), and was therefore excluded from the manually drawn reference line, seen in Figure 14b. The GBA again only detects turbulence wedges with \(\text{CNR}>3\), while the MBA again detects all turbulence wedges, see Figure 14c. This means that the GBA can detect wedges with a lower CNR in this thermogram than in the thermogram depicted in Figure 13. This is probably due to the preprocessing of the GBA, which applies a histogram equalization to each investigated thermogram. Therefore, different thermograms can be optimized to different degrees, leading to differences between thermograms with regard to the CNR of still detectable turbulence wedges. The MBA does not exhibit this fluctuation in the performance as no preprocessing is needed, which makes the MBA more reliable. In Table 4, the summed areas of all wedges as determined by each algorithm and the manually estimated reference area are listed. Even though the GBA does not detect every turbulence wedge, the found areas of each wedge are determined to be much larger than they actually are due to the image noise, which leads to a remaining relative deviation of the total area of \(-5.1\%\). However, a quantitative comparison of the determined area of each  
Figure 13: Thermogram of a segment of a rotor blade of an in-service wind turbine with \(N=5\) turbulence wedges.