# Estimation of Wind Turbine Blade Icing Volume Based on Binocular Vision
## 3 Experimental Design and Results Analysis  
### Noise Resistance Experiment  
To evaluate the performance and robustness of the proposed algorithm, experiments were conducted using stereo image pairs from the Middlebury dataset [32], as shown in Figure 8. One of the key evaluation metrics is the mismatch rate, which is defined as:  
\[P_{\text{PBM}}=\frac{1}{N}\sum_{(x,y)}[|d_{e}(x,y)-d_{r}(x,y)|>\sigma_{d}], \tag{26}\]  
where \(N\) denotes the total number of effective pixels in the evaluated image region. Here, \(d_{e}(x,y)\) represents the disparity values estimated by the algorithm, and \(d_{r}(x,y)\) refers to the ground truth disparity provided by the dataset. The parameter \(\sigma_{d}\) is the disparity threshold. This evaluation approach ensures a fair and consistent comparison of the algorithm's accuracy by focusing on disparity deviations beyond the acceptable range.  
The implementation of the algorithm was carried out using C++ on the Visual Studio 2022 development platform. Image operations, such as reading, displaying, and saving, were performed using the OpenCV open-source vision library. The experimental hardware environment consisted of an AMD Ryzen 7 3750H processor and 16 GB of RAM.  
To evaluate the noise resistance of the algorithm, salt-and-pepper noise and Gaussian noise were added to the standard test images. For salt-and-pepper noise, the coverage rates were set to 3%, 6%, 9%, and 12%. For Gaussian noise, the standard deviations were set to 2, 4, 6, and 8. The disparity maps generated from the noisy images were then used to calculate the mismatch rate in the non-occluded regions. These results were compared with those obtained using the AD-Census algorithm [24] and the SGM algorithm [33]. The final experimental results are summarized in Tables 1-3.  
Based on Tables 1-3, when no noise is introduced, the average mismatching rate of the improved algorithm proposed in this paper is significantly lower compared to other algorithms. As the noise density increases, the mismatching rates of all algorithms also rise. Notably, the SGM algorithm exhibits the largest increase, indicating its highest sensitivity to noise addition, whereas the improved algorithm in this study shows the smallest increase, indicating that the proposed algorithm is more robust than the SGM algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **0** & **3** & **6** & **9** & **12** \\ \hline SGM & 4.99 & 5.72 & 7.32 & 8.16 & 9.87 \\ AD-Census & 4.57 & 5.28 & 5.88 & 6.92 & 8.76 \\ Proposed algorithm & 3.97 & 3.99 & 4.21 & 4.83 & 6.52 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mismatch rates of different algorithms after adding salt-and-pepper noise (%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **0** & **2** & **4** & **6** & **8** \\ \hline SGM & 4.99 & 8.78 & 11.18 & 14.09 & 16.57 \\ AD-Census & 4.57 & 6.20 & 8.30 & 10.15 & 12.21 \\ Proposed algorithm & 3.97 & 5.20 & 7.21 & 8.97 & 10.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mismatch rates of different algorithms after adding Gaussian noise (\(\sigma\)).  
Figure 8: Middlebury stereo evaluation dataset. (**a**) The left image of the stereo pair and the ground truth map of the Cones dataset; (**b**) the left image of the stereo pair and the ground truth map of the Teddy dataset.  
which is markedly lower than that of the other comparison algorithms, demonstrating strong noise resistance. Additionally, the proposed algorithm achieves competitive runtime performance with an average running time of 3.201 s, which is close to the AD-Census algorithm and much faster than the SGM algorithm, achieving a good balance between accuracy and efficiency.