# Research on Road Pattern Recognition of a Vision-Guided Robot Based on Improved-YOLOv8
## 2 Experiment and Data Acquisition Processing
### Experimental Design
#### 2.1.3 Specific Selection and Sampling of the Pavement Environment  
The road pattern image shooting process was completed by two people, one of whom was responsible for camera scene selection and adjustment, as well as control of height and angle. In order to improve the randomness of the recognition data, some appropriate adjustments were made to height and angle during sampling. The other person was responsible for using a computer program to complete the shooting and saving. Finally, the photos were summarized and screened.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Resolution Ratio** & **Frame Rate** &
\begin{tabular}{c} **Whether There Is** \\ **Distortion** \\ \end{tabular} & **Driving Mode** & **Focusing Mode** \\ \hline
640 \(\times\) 480 & 30 frames per second & Distortionless & USB interface & Manual focus possible \\ \hline
**Power supply mode** & **Type** & **Lens size** & **Wide angle** & \\ \hline USB power supply & DF200-1080p & 2.8 mm & 100\({}^{\circ}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Camera parameters.  
Figure 1: Pavement image capture system.  
In the process of selecting different pavements, asphalt road pavements are more common, most of which are intact and have high similarity. In order to diversify the collected image data, different roads, different angles, different traffic signs, and different environments on both sides of the pavement were photographed during the sampling process. At the same time, some fallen leaves and branches were added to make the images more distinguishable.  
During the masonry road sampling process, different masonry shapes (diamond and rectangle) and color (gray, red, and yellow) were selected, while some fallen leaves were added to create different scenarios to achieve a variety of masonry road conditions for sampling and to improve the randomness of the data samples.  
During the sampling of concrete roads, due to the great difference in the degree of damage to concrete roads, concrete roads were defined as good roads, slightly damaged pavements, and heavily damaged pavements. Then, according to the pavement conditions in different weather conditions, combined with the internal and surrounding environment of the school, the concrete roads were divided into multiple categories for shooting. Good concrete pavement also has smooth and rough points; some deciduous flowers were added to distinguish them; according to the different roughnesses of the surfaces, slightly damaged pavements were also collected for diversity. Due to the serious surface damage of the heavily damaged pavements, there were many obvious depressions, so waterlogged pavement after rain was selected for shooting.  
The road condition of sand pavements is more homogenized, so during the shooting process, selective sampling was carried out in different locations and under different road conditions around the gravel roads, combining the vehicles parked on the road and the flowers and plants growing nearby to realize the diversity of sand pavement sampling.  
In the process of soil road shooting, the color, texture, and hardness of soil and the presence or absence of plant growth were used as the basis for classification. Some soil roads are seriously damaged, and there will be water waterlogged after rain, which is suitable for shooting waterlogged, heavily damaged pavement. By sampling soil roads in many places, combined with the change of pavement state in different weather conditions, the image sampling of various types of soil roads was completed.  
After completion of each shooting, summarize, classify, and filter, create conditions to increase multi-scene shooting according to actual conditions, and finally unify the folders with English names, unify the categories, delete the pictures with serious homogenization, and retain the actual effective pictures as the basic experimental data for the recognition model.