## 4 Experiments
### Comparison with Advanced Technology  
To be fair, we only compared the reflection region removal method based on relative motion [22], the reflection region removal method based on machine learning networks [4], and the reflection region removal method proposed in this paper that combines image completion and lighting compensation. Please note that our method is not designed to achieve optimal reaction time but to emphasize the advantages of our simple structure and the adaptability of the method in changing environments. Discriminant analysis. Our method was used to validate the adaptability of each method in two different environments (light intensity \(\sigma_{1}=50 lx\), \(\sigma_{2}=150 lx\)). We collected a real car video frame sequence for our experiment, which resulted in a dataset of 2318 reflection images. Our method, as shown in Table 1, has an average reduction of +1.34% in recognition and decision-making time compared to the other two methods, demonstrating its practical value. However, our method still lags behind machine learning networks in terms of the detection time for reflection regions. Due to the similarity between the accuracy evaluation criteria of the reflection area removal method and the mechanism of obstacle recognition, this paper evaluates the advantages and disadvantages of reflection removal methods based on the interference of the reflection area on the normal recognition of obstacles during the obstacle recognition process and the traditional normalized product correlation (NCC) evaluation criteria for image matching. The calculation formulas for the accuracy evaluation index of the reflection area recognition are \(DT=\frac{a_{i}}{a_{i}+c_{i}+d_{i}}\) and \(RT=\frac{a_{i}+c_{i}}{a_{i}+c_{i}+d_{i}}\). The data in Table 2 include the number of video frame images in which each method detects a reflection area in the same video, which can be divided into the number of images that can detect the reflection area and the number of images that cannot detect the reflection area (\(c_{i}\)). By setting the reflection result threshold \(Q_{i}\), the number of samples that can recognize the reflection area and the number of pixels in the reflection area is greater than the threshold, which is denoted as \(a_{i}\); the number of samples that have a reflection area and the number of pixels in the reflection area being less than the threshold is denoted as \(b_{i}\); the number of samples that have a reflection area greater than the threshold, but the detection result has no reflection area is denoted as \(c_{i}\); and the number of samples that have a reflection area less than the threshold, but the detection result has no reflection area is denoted as \(d_{i}\). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Detection Time/s** & **Identification Time/s** \\ \hline Reflection removal based on relative motion compensation & 2.498 & 3.246 \\ \hline Reflection Removal Method for Machine Learning Networks & 2.452 & 2.783 \\ \hline Combination of image completion and illumination & 2.632 & 2.674 \\ \hline Static Video Reflection Removal Method [33] & 2.744 & 2.863 \\ \hline Black box video reflection removal [34] & 2.531 & 2.700 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of reflection area recognition and detection time. Adaptability comparison. Table 2 shows that we evaluated our algorithm by calculating the normalized cross correlation (NCC) of the ground truth decomposition we recovered. The NCCs of the reflection layer we separated were 0.89236, 0.86982, 0.91563, 0.90642, and 0.88959, while the NCCs of the background layer were 0.86624, 0.87431, 0.90875, 0.90433 and 0.89746, respectively. The RT, DT, and NCC of the reflection region removal method based on image completion and lighting compensation are higher than the other four methods. In this paper, it was observed that differences in lighting intensity in various driving environments can lead to false detections and missed detections.