## 4 Results and Discussion
### Features Classification Results
However, its slight underperformance in the crosswalk-type feature classification suggests that while deeper networks can capture complex patterns, they may require further tuningor additional training data specific to certain features. In contrast, the VGG-19 model, known for its deeper but more uniform architecture, often requires more computational resources and training time, which might explain its relatively lower performance in our study. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & \\ \hline \multirow{2}{*}{**Feature**} & \multirow{2}{*}{**\# Epoch**} & \multirow{2}{*}{**Learning**} & \multirow{2}{*}{**Fold**} & \multicolumn{3}{c}{**Mean Accuracy**} & \multicolumn{3}{c}{**Mean F-Score**} \\  & & & **AlexNet** & **VGG-19** & **ResNet-50** & **AlexNet** & **VGG-19** & **ResNet-50** \\ \hline \multirow{6}{*}{Intersection Type (Three-leg or Four-leg)} & \multirow{6}{*}{1} & 1 & 0.8236 & 0.8323 & 0.8124 & 0.8087 & 0.8617 & 0.7713 \\  & & 2 & 0.8583 & 0.8639 & 0.7714 & 0.7724 & 0.8087 & 0.8241 \\  & & 3 & 0.8609 & 0.8065 & 0.8581 & 0.8541 & 0.8267 & 0.8339 \\  & & 4 & 0.8576 & 0.7790 & 0.8881 & 0.8806 & 0.7793 & 0.7920 \\  & & 5 & 0.8443 & 0.8033 & 0.8242 & 0.8323 & 0.7724 & 0.8115 \\ \cline{2-8}  & & **Mean** & **0.85** & **0.82** & **0.83** & **0.83** & **0.81** & **0.81** \\ \cline{2-8}  & & & 1 & 0.8150 & 0.8870 & 0.8614 & 0.8876 & 0.8574 & 0.8743 \\  & & 2 & 0.8077 & 0.7541 & 0.8957 & 0.8274 & 0.7733 & 0.9016 \\  & & 3 & 0.8514 & 0.7731 & 0.7414 & 0.8385 & 0.7832 & 0.7374 \\  & & 4 & 0.9014 & 0.8571 & 0.7793 & 0.8379 & 0.8433 & 0.7932 \\  & & 5 & 0.8299 & 0.8024 & 0.8267 & 0.8319 & 0.8317 & 0.8376 \\ \cline{2-8}  & & **Mean** & **0.84** & **0.81** & **0.82** & **0.84** & **0.82** & **0.83** \\ \hline \multirow{6}{*}{Intersection Angle (Skewed or Unskewed)} & \multirow{6}{*}{0.01} & 1 & 0.7206 & 0.8870 & 0.8817 & 0.7458 & 0.8933 & 0.8937 \\  & & 2 & 0.7951 & 0.7152 & 0.7224 & 0.8032 & 0.7035 & 0.7385 \\  & & 3 & 0.8705 & 0.7224 & 0.7874 & 0.8532 & 0.7354 & 0.7743 \\  & & 4 & 0.9452 & 0.8381 & 0.8806 & 0.9186 & 0.8283 & 0.8965 \\  & & 5 & 0.8976 & 0.8258 & 0.8581 & 0.8877 & 0.8396 & 0.8624 \\ \cline{2-8}  & & **Mean** & **0.85** & **0.80** & **0.83** & **0.84** & **0.80** & **0.83** \\ \cline{2-8}  & & 1 & 0.8453 & 0.8881 & 0.8020 & 0.8274 & 0.8933 & 0.7932 \\  & & 2 & 0.8671 & 0.7921 & 0.8019 & 0.8584 & 0.8176 & 0.8128 \\  & & 3 & 0.8972 & 0.8835 & 0.7472 & 0.8722 & 0.8452 & 0.7530 \\  & & 4 & 0.7914 & 0.8105 & 0.7138 & 0.8054 & 0.8029 & 0.7033 \\  & & 5 & 0.9057 & 0.8632 & 0.8065 & 0.8828 & 0.8131 & 0.7990 \\ \cline{2-8}  & & **Mean** & **0.86** & **0.85** & **0.77** & **0.85** & **0.83** & **0.77** \\ \cline{2-8}  & & 1 & 0.8917 & 0.8120 & 0.7715 & 0.8990 & 0.8032 & 0.7844 \\  & & 2 & 0.8718 & 0.7225 & 0.8123 & 0.8779 & 0.7378 & 0.8276 \\  & & 3 & 0.8178 & 0.7987 & 0.8681 & 0.8094 & 0.7854 & 0.8590 \\  & & 4 & 0.8316 & 0.8133 & 0.8533 & 0.8366 & 0.8118 & 0.8429 \\  & & 5 & 0.8043 & 0.8713 & 0.7789 & 0.8213 & 0.8794 & 0.7938 \\ \cline{2-8}  & & **Mean** & **0.84** & **0.80** & **0.82** & **0.85** & **0.80** & **0.82** \\ \cline{2-8}  & & 1 & 0.8410 & 0.8103 & 0.7785 & 0.8597 & 0.8267 & 0.7832 \\  & & 2 & 0.8747 & 0.7503 & 0.7907 & 0.8896 & 0.7590 & 0.8003 \\  & & 3 & 0.8713 & 0.7763 & 0.8427 & 0.8694 & 0.7793 & 0.8390 \\  & & 4 & 0.8642 & 0.8032 & 0.8013 & 0.8704 & 0.8094 & 0.8093 \\  & & 5 & 0.8859 & 0.8263 & 0.8215 & 0.8873 & 0.8303 & 0.8298 \\  & & **Mean** & **0.87** & **0.79** & **0.81** & **0.88** & **0.80** & **0.81** \\ \cline{2-8}  & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: (**a**) Comparison training results of three CNN models over 50 epochs for all features classification. (**b**) Comparison validation results of three CNN models over 50 epochs for all features’ classification. As depicted in Figure 2a,b, it is crucial to highlight the evaluation of the mean training and validation loss values across 50 epochs for all CNN models.