## 4 Results and Discussion
### Features Classification Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & **(b)** & & \\ \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}{*}{
\begin{tabular}{c} Intersection \\ Type (Three-leg \\ or Four-leg) \\ \end{tabular} } & 1 & 0.8132 & 0.8133 & 0.8722 & 0.86 & 0.8014 & 0.8237 \\  & 2 & 0.8905 & 0.8326 & 0.8324 & 0.886 & 0.7724 & 0.8449 \\  & 3 & 0.8745 & 0.8333 & 0.8258 & 0.8321 & 0.8158 & 0.8322 \\  & 4 & 0.8546 & 0.8090 & 0.8523 & 0.8412 & 0.8123 & 0.8386 \\  & 5 & 0.8235 & 0.8156 & 0.8210 & 0.8934 & 0.8261 & 0.8159 \\ \cline{2-8}  & \multirow{2}{*}{**Mean**} & **0.85** & **0.82** & **0.84** & **0.86** & **0.81** & **0.83** \\ \cline{3-8}  & & 1 & 0.8294 & 0.8252 & 0.8468 & 0.8392 & 0.8101 & 0.8595 \\  & 2 & 0.8683 & 0.7721 & 0.8275 & 0.8886 & 0.7794 & 0.8190 \\ Road Type & 3 & 0.8832 & 0.7032 & 0.8479 & 0.9031 & 0.7589 & 0.8152 \\ (Divided or \\ Undivided) & 4 & 0.8832 & 0.8437 & 0.8194 & 0.8610 & 0.8290 & 0.8476 \\  & 5 & 0.8293 & 0.8091 & 0.8003 & 0.8591 & 0.8368 & 0.8236 \\ \cline{2-8}  & \multirow{2}{*}{**Mean**} & **0.86** & **0.79** & **0.83** & **0.87** & **0.80** & **0.83** \\ \cline{3-8}  & & 1 & 0.8935 & 0.8193 & 0.8863 & 0.8678 & 0.8596 & 0.8991 \\  & 2 & 0.8832 & 0.8044 & 0.8056 & 0.8270 & 0.7454 & 0.7594 \\  & 3 & 0.9166 & 0.8145 & 0.8489 & 0.8489 & 0.7051 & 0.8536 \\  & 4 & 0.9455 & 0.8487 & 0.8482 & 0.8923 & 0.8361 & 0.8479 \\  & 5 & 0.9044 & 0.8589 & 0.8630 & 0.8933 & 0.8427 & 0.8377 \\
**50** & **0.01** & **0.83** & **0.85** & **0.87** & **0.80** & **0.84** \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Crosswalk \\ Type (Marked) or Unmarked) \\ \end{tabular} } & 1 & 0.8573 & 0.8792 & 0.7829 & 0.8291 & 0.8352 & 0.7711 \\  & 2 & 0.8905 & 0.8093 & 0.7536 & 0.8790 & 0.8168 & 0.8142 \\  & 3 & 0.9167 & 0.8745 & 0.7033 & 0.8938 & 0.8379 & 0.7754 \\  & 4 & 0.8590 & 0.8293 & 0.7003 & 0.8268 & 0.7737 & 0.7659 \\  & 5 & 0.9279 & 0.8476 & 0.7896 & 0.8938 & 0.8854 & 0.7937 \\ \cline{2-8}  & \multirow{2}{*}{**Mean**} & **0.89** & **0.85** & **0.75** & **0.86** & **0.83** & **0.78** \\ \cline{3-8}  & & 1 & 0.9276 & 0.7505 & 0.7598 & 0.8908 & 0.8019 & 0.8097 \\  & 2 & 0.9033 & 0.7004 & 0.7936 & 0.8775 & 0.8390 & 0.8033 \\  & 3 & 0.8537 & 0.8 & 0.8479 & 0.8270 & 0.8011 & 0.8702 \\  & 4 & 0.8268 & 0.8277 & 0.8377 & 0.8395 & 0.8194 & 0.8396 \\  & 5 & 0.8591 & 0.8518 & 0.8275 & 0.8495 & 0.8254 & 0.8042 \\ \cline{2-8}  & \multirow{2}{*}{**Mean**} & **0.87** & **0.79** & **0.81** & **0.86** & **0.82** & **0.83** \\ \cline{3-8}  & & 1 & 0.8599 & 0.8190 & 0.8101 & 0.8674 & 0.8398 & 0.8098 \\ \cline{3-8}  & & 2 & 0.9095 & 0.7679 & 0.8257 & 0.9276 & 0.7908 & 0.8126 \\ \cline{3-8}  & 3 & 0.8895 & 0.7891 & 0.8312 & 0.9190 & 0.7657 & 0.8487 \\ \cline{3-8}  & 4 & 0.8594 & 0.8190 & 0.8191 & 0.8990 & 0.8195 & 0.7993 \\ \cline{3-8}  & 5 & 0.9175 & 0.8567 & 0.8310 & 0.8855 & 0.8291 & 0.8376 \\ \cline{3-8}  & \multirow{2}{*}{**Mean**} & **0.89** & **0.81** & **0.82** & **0.90** & **0.81** & **0.82** \\ \cline{3-8}  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._The computational time required for training and validation is critical in evaluating model performance. The AlexNet model also showcases an advantage in this regard, as shown in Figure 3, achieving faster convergence compared to other models. Despite the task's complexity, AlexNet maintains a balance between high accuracy and efficient processing time. This efficiency in computational time is particularly beneficial for real-time applications and large-scale datasets, where prompt and reliable feature extraction is essential. Figure 2: (**a**) Mean training loss of all CNN models for all features’ classifications. (**b**) Mean validation loss of all CNN models for all features classifications.