## 3 Methods
### Sign Recognition  
In this study, the Jetson AGX Xavier equipped with a high-field-of-view fisheye camera was used as the image processing unit, specifically designed to accelerate the YOLOv4-tiny model. Utilizing NVIDIA's CUDA and cuDNN libraries, YOLOv4-tiny significantly improves the speed and accuracy of road sign recognition in all aspects of various conditions, including, but not limited to, strong sidelight, low light, and other special light interference. As demonstrated in Figure 5, YOLOv4-tiny consistently recognizes road signs in diverse environments, maintaining its performance even under extreme and challenging conditions. The robustness of the YOLOv4-tiny model in handling a wide range of lighting and environmental scenarios is crucial for the reliable functioning of autonomous navigation systems. It ensures that the system can operate effectively not just in ideal conditions but also in real-world scenarios where lighting and visibility can vary significantly. To investigate the specific effects of data augmentation on the performance of YOLOv4-tiny models, a series of experiments were designed in this study to evaluate the effectiveness of various data augmentation methods. The experiments used a set of non-intersecting training and validation data and applied 8 types of data augmentation techniques built into YOLOv4-tiny: 1. Saturation, 2. Exposure, 3. Hue, 4. Blur, 5. Mosaic, 6. Gaussian, 7. Random Resizing, and 8. Cropping. By randomly combining these techniques, 28 different configurations of datasets were generated, as listed in Table 3.  
The experimental results showed that the application of data augmentation does not always have a proportional relationship to model performance. As indicated in Table 4, some augmentation techniques, when used individually, have limited or even negative effects on the model's improvement. This study found that specific combinations of data augmentation techniques could significantly enhance model performance, especially under limited data conditions. For instance, data augmentation methods involving size variation proved particularly effective, as they increase the model's ability to recognize objects of various sizes, which is beneficial in situations where the amount of data is unknown. Figure 5: Two results of the road sign recognition: (**a**) schematic diagram of detecting left turn road sign; (**b**) schematic diagram of actual detection situation. \begin{table}
\begin{tabular}{c l} \hline \hline
**Number** & \multicolumn{2}{c}{**Data Augmentation Methods**} \\ \hline
1 & None \\
2 & Saturation \\
3 & Hue \\
4 & Gaussian noise (Gaussian) \\
5 & Random \\
6 & Blur \\
7 & Crop \\ \hline
8 & Mosaic, Hue \\
9 & Blur, Gaussian \\ \hline
10 & Crop, Random, Mosaic \\
11 & Exposure, Hue, Random \\
12 & Exposure, Blur, Gaussian \\
13 & Random, Crop, Saturation \\
14 & Blur, Mosaic, Gaussian \\
15 & Saturation, Exposure, Hue \\ \hline
16 & Crop, Saturation, Random, Mosaic \\
17 & Hue, Blur, Mosaic, Random \\
18 & Exposure, Blur, Random, Crop \\
19 & Mosaic, Gaussian, Random, Crop \\
20 & Saturation, Hue, Blur, Gaussian \\
21 & Saturation Hue, Blur, Random, Crop \\ \hline
22 & Exposure, Mosaic, Gaussian, Random, Crop \\
23 & Saturation, Exposure, Hue, Gaussian, Random \\ \hline
24 & Hue, Blur, Mosaic, Gaussian, Random, Crop \\
25 & Saturation, Exposure, Hue, Blur, Gaussian, Crop \\
26 & Saturation, Exposure, Hue, Blur, Mosaic, Gaussian \\
27 & Saturation, Hue, Blur, Mosaic, Gaussian, Random \\ \hline
28 & ALL \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of data augmentation methods. Further analysis revealed a 52.14% improvement in model performance using all data augmentation techniques. However, the details of the analysis show that not all data augmentations will have positive effects, and some specific techniques may counteract others when applied together comprehensively. This indicates that choosing the appropriate data augmentation strategy is more important than simply increasing the number of augmentation techniques.