## 4 Experimental Results and Discussion
### Model Testing  
#### 4.4.1 Comparison of the mAPs of YOLOv7 and Mask R-CNN in Each Model  
Table 19 shows the mAP results of YOLOv7 and Mask R-CNN in each model. The mAPs of YOLOv7 and Mask R-CNN in the proposed model (M4) were the highest among all models. The APs with respect to Class A and Class B were also the highest among all models. \begin{table}
\begin{tabular}{c c c} \hline \hline \begin{tabular}{c} **Condition** \\ \end{tabular} & \begin{tabular}{c} **Category** \\ \end{tabular} & \
\begin{tabular}{c} **Class A** \\ **(Speed Limit Signs)** \\ \end{tabular} &
\begin{tabular}{c} **Class B** \\ **(Rockfalls, Potholes, and Car Crashes)** \\ \end{tabular} \\ \hline Daytime & 1854 & 252 \\ \hline Nighttime & 441 & 147 \\ \hline Rainy day & 273 & 160 \\ \hline Foggy day & 132 & 37 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Statistics for the number of Class A and Class B images used in the training set with respect to weather condition. \begin{table}
\begin{tabular}{c c c} \hline \hline \begin{tabular}{c} **Condition** \\ \end{tabular} & \begin{tabular}{c} **Category** \\ \end{tabular} & \
\begin{tabular}{c} **Class A** \\ **(Speed Limit Signs)** \\ \end{tabular} &
\begin{tabular}{c} **Class B** \\ **(Rockfalls, Potholes, and Car Crashes)** \\ \end{tabular} \\ \hline Daytime & 186 & 25 \\ \hline Nighttime & 55 & 15 \\ \hline Rainy day & 27 & 16 \\ \hline Foggy day & 13 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Statistics for the number of Class A and Class B images used in the test set with respect to weather condition. \begin{table}
\begin{tabular}{c c c} \hline \hline \begin{tabular}{c} **Category** \\ \end{tabular} & \
\begin{tabular}{c} **Class A** \\ **(Speed Limit Signs)** \\ \end{tabular} &
\begin{tabular}{c} **Class B** \\ **(Rockfalls, Potholes, and Car Crashes)** \\ \end{tabular} \\ \hline Daytime & 1854 & 252 \\ \hline Nighttime & 441 & 147 \\ \hline Rainy day & 273 & 160 \\ \hline Foggy day & 132 & 37 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Statistics for the number of Class A and Class B images used in the training set with respect to weather condition. The mAP of M1 was a relatively low 79.60%, which is decent if solely based on accuracy. It was, however, the lowest among all models, which shows that the design of this model was inferior to the others. A probable reason is because of the abundance of classifications (which were all handed by YOLOv7 only), as well as the lack of training on poor weather conditions and rotated images. The use of a more enriched dataset or image classification beforehand are two ways that could compensate for the shortcomings of this model. For M2, we divided the Class A (speed limit signs) and Class B (rockfalls, potholes, and car crashes) objects first using YOLOv7 and then subdivided Class A into Class A_30, Class A_40, Class A_50, Class A_60, Class A_70, Class A_80, Class A_90, Class A_100, and Class A_110 using Mask R-CNN. This method reduced the processing load of YOLOv7. The mAPs of YOLOv7 and Mask R-CNN in M2 were 74.80% and 83.10%, respectively, indicating that it was more efficient at recognizing speed limits than M1. By using the M2 training method in addition to Gaussian noise and rotating the angle to augment the variety of images, M3 simulated poor weather conditions as well as scenarios in which the detected objects were obscured, slanted, or twisted. The mAPs of YOLOv7 and Mask R-CNN in M3 rose to 79.30% and 87.30%, respectively. This shows that it is necessary to added noise and interference in order to successfully train a model. M4 was based on the M3 training method but also included Canny edge detection to enhance the image-processing training, Canny edge detection accentuated the contours of the detected objects, especially during rainy or nighttime conditions, thus enhancing the objects' features. The experiments showed that the mAPs of YOLOv7 and Mask R-CNN increased to 85.80% and 89.30%, respectively.