# A Low Cost Vision-Based Road-Following System for Mobile Robots
## 6 Experiments  
The proposed road-following system is evaluated and tested on a variety of unstructured roads under different lighting and weather conditions. The condition includes both the environmental conditions as lighting and weather, and the structure condition like road material and border state. The system has also been tested under variable illumination. In this case, the same road has been surveyed with different lighting conditions during different hours on the same day, such as natural daylight, sunshine, sunset, etc. The robot shown in Figure 1 is used for the road-following test in dynamic environments with maximum speed 0.5 m/s. More than 27,000 images are taking into account, which are grouped into 13 types of roads. During the image processing, the correct recognition is defined as a detection trust value higher than 90% according to (5) and (6). Hence, the correct rate is defined as the number of frames in which the road tracking position is recognized correctly over the total number of image frames. The statistical results of the experiment are listed in Table 2. As shown in Table 2, the recognition rate median value is about 98.7%, with a minimum recognition rate of 79.27%. Therefore, the system has a recognition rate of 99.78% in the optimal case; in the regular cases, a recognition rate of about 98.7%; and in the worst cases, a recognition rate of 79.27%. \begin{table}
\begin{tabular}{c c c c} \hline
**Road Type Number** & **Characteristic** & **Number of Frames** & **Correct Rate** \\ \hline
1 & Bikeway, daylight, cement road & 1912 & 97.8\% \\
2 & Parkway, sunset, cement road & 734 & 97.4\% \\
3 & Parkway, daylight, asphalt road & 3660 & 99.1\% \\
4 & Bikeway, sunset, asphalt road & 1555 & 92.9\% \\
5 & Parkway, sunshine, asphalt road & 951 & 95.7\% \\
6 & Bikeway, daylight, asphalt road & 1645 & 99.39\% \\
7 & Parkway, daylight, dirt road & 2123 & 99.15\% \\
8 & Sidewalk, daylight, brick road & 4366 & 98.35\% \\
9 & Sidewalk, daylight, dirt road & 3069 & 99.24\% \\
10 & Sidewalk, sunset, brick road & 387 & 98.7\% \\
11 & Bikeway, sunshine, asphalt road & 386 & 79.27\% \\
12 & Sidewalk, daylight, cement road & 2066 & 99.61\% \\
13 & Sidewalk, daylight, asphalt road & 1884 & 99.78\% \\ \hline \end{tabular}
\end{table} 
Table 2: The correct rate. The image processing is run on a PC with an Intel Core i7 CPU at 2.93 GHz in Ubuntu 16.04. The software for image processing is written with C++ using OpenCV library. The proposed system works at the frame rate of 10 Hz. The computation time for each sub-process is clearly indicated in Table 3.  
The runtimes are analyzed based on more than 24,700 images. The required computation time for image processing is shown in Table 3. The values that are shown in the column \"Time (ms)\" represent the average value of each sub-process during the road following. The column \"Percentage (%)\" shows the percentage of each sub-process in relation to the complete detection process. As can be seen in Table 3, the sub-processes that require longer run times are the extraction of straight lines and the IPM. The extraction of the road image and the borders validation are the faster ones. The entire runtime required to processes one single image is about 74.5 ms. In other words, the value represents a frame rate of about 13.4 Hz. According to literature [20], the deep fully convolutional neural network SegNet for road scene segmentation needs more computational resources and trains much more slowly. The computational time of the forward pass and backward pass for SegNet architecture is 422.50 ms and 488.71 ms, respectively, i.e., a higher time cost than the image processing of our proposed method. A sequence of frames showing the detection and road tracking target is presented in Figure 13. The red lines are the estimated road borders. The black square is the road tracking target in the image.