## 3 Experiment and Discussion
### Evaluation of the Improved Image Quilting  
In this study, the Chinese expressway is used as the research object. According to the standard, the width of the lane on highway is 15 cm. Usually, the lateral distance between vehicle and the lane boundary is no more than 150 cm. Hence, the maximum distance is set to 150 cm. After camera calibration, the length of 1 cm in the world coordinate system corresponds to 2.13 pixels in the image. As a consequence, the maximum distance \(d_{\text{max}}\) and width of the lane in the image are 31.95 and 319.5 pixels, respectively. Therefore, the road size is set to 360 \(\times\) 360 pixels. Moreover, the pixel distance between the lane strip and asphalt is \(d_{i}\) (\(d_{i}\) [0,320]), which is used as a label in the process of image composition. There may be no lane in the natural traffic scene, and the label \(d_{i}\) = 321 is used to indicate that condition. The code of improved Image Quilting was programmed in MATLAB 2016 and MySQL 5.5. The images in real scene are captured by a camera on the rear-view mirror in the circular highway of Chongqing. In order to ensure the purity of the original images, they were collected in a sunny day without any shadow on the road. After the image acquisition was completed, we selected 1200 images for real scenes for compositing and similarity comparisons. According to the parameters that are presented in Table 1, 100 lane and asphalt images were synthesized. The average synthetic time of lane and asphalt by improved Image Quilting and the algorithms presented in [31; 34] were shown in Figure 8a,b. When the value of \(\lambda_{0}\) is small, then the probability of one-time successful selection of the required texture block in a random way is approximately 100%. The improved Image Quilting algorithm needs to calculate gradient and RGB information simultaneously, and the computational amount would be increased. Accordingly, the synthesis takes longer time. With the increase of \(\lambda_{0}\), the probability of finding the required texture block using the algorithms presented in [31; 34] was decreased and the synthetic time was increased drastically. As the required texture block can be searched in the database directly, the synthetic time in the improved Image Quilting is constant no matter how big \(\lambda_{0}\) is. In fact, when a large number of images need to be synthesized, the speed can be improved greatly through parallel computing in MATLAB. An image with the size of 360 \(\times\) 360 pixels can be synthesized \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Image Type**} & **Texture Block Size** & **Block** & \multirow{2}{*}{\(\mathbf{\alpha}\)} & **Output Image Size** & **Synthesis** \\  & **(Pixel \(\times\) Pixel)** & **Number** & & **(Pixel \(\times\) Pixel)** & **Time (s)** \\ \hline Lane & 8 \(\times\) 30 & 2500 & 0.91 & 0.09 & 32 \(\times\) 360 & 7.73 \\ Road & 30 \(\times\) 30 & 2500 & 0.65 & 0.35 & 360 \(\times\) 360 & 20.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameters used for improved Image Quilting to synthesize images. Figure 8: The mean synthesis time of lane in (**a**) and asphalt in (**b**) by the algorithm in [31; 34], and by the improved Image Quilting in this study. The similarity metric along the horizontal axis is \(\lambda_{0}\). in 3 s. Therefore, a large number of high-quality images can be synthesized rapidly using the algorithm that is presented in this study. To ensure the validity of the recognition model, the generated label images should be sufficiently similar with the real scene images. In this study, the similarity was evaluated by the full reference image quality assessment [43]. First, 100 images from four categories of road images were generated according to the damage degree [44].