## 3 Experimental Results
### Evaluation of Vehicle Type and Color Classification Accuracy
Experiments on vehicle type classifiers indicate that the optimal training option was to create a set of options to train the neural network using the Adam optimization function, use a mini-batch with 64 observations for each iteration, and set the initial learning rate to 0.0001. Table 3 presents the results of the vehicle type classifier experiment. Among the training options used, the sixth training set [Solver = Adam optimization function, initial learning rate = 0.00001, internal batch size = 64] and the ninth training set [Solver = RMSProp optimization function, initial learning rate = 0.0001, internal batch size = 30] resulted in the same accuracy. However, the ninth training set required the shortest learning time. Experiments conducted on vehicle color classifiers suggest that the optimal training option is to create a set of options to train the neural network using the RMSProp optimization function, use a minibatch with 64 observations for each iteration, and set the initial learning rate to 0.00001. The training progress of the classifier using the optimal NN hyperparameters and training options is shown in Figure 11. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Solver** & **Mini-Batch Size** & **Initial Learning Rate** & **Average Precision** \\ \hline
1 & & 64 & 0.0001 & 94.77 \\
2 & sdgm & 64 & 0.00001 & 89.09 \\
3 & & 128 & 0.0001 & 93.78 \\
4 & & 128 & 0.00001 & 94.50 \\ \hline
5 & & 64 & 0.0001 & 96.72 \\
6 & & 64 & 0.00001 & 97.22 \\
7 & & 128 & 0.0001 & 96.89 \\
8 & & 128 & 0.00001 & 96.91 \\ \hline
9 & & 64 & 0.0001 & 97.22 \\
10 & rmsprop & 64 & 0.00001 & 96.76 \\
11 & & 128 & 0.0001 & 97.00 \\
12 & & 128 & 0.00001 & 95.50 \\ \hline \hline \end{tabular} (a)  
\begin{tabular}{c c c c} \hline \hline
**No.** & **Solver** & **Mini-Batch Size** & **Initial Learning Rate** & **Average Precision** \\ \hline
1 & & 64 & 0.0001 & 88.24 \\
2 & sdgm & 64 & 0.00001 & 84.38 \\
3 & & 128 & 0.0001 & 86.55 \\
4 & & 128 & 0.00001 & 81.36 \\ \hline
5 & & 64 & 0.0001 & 83.85 \\
6 & & 64 & 0.00001 & 89.59 \\
7 & & 128 & 0.0001 & 88.57 \\
8 & & 128 & 0.00001 & 90.27 \\ \hline
9 & & 64 & 0.0001 & 88.52 \\
10 & rmsprop & 64 & 0.00001 & 95.39 \\
11 & & 128 & 0.0001 & 89.45 \\
12 & & 128 & 0.00001 & 94.94 \\ \hline \hline \end{tabular} (b)  
\end{table} 
Table 3: Experimental results of vehicle type and color classification by neural network hyperparameters and training options. (a) results of vehicle type classification, (b) results of vehicle color classification. Figure 12 shows the confusion matrix for the vehicle type and color classifier, calculated for both the training and validation sets. The matrix shows the accuracy of classification by vehicle type and color class. The normalized column summary in Figure 12 shows the percentage of correctly and incorrectly classified observations for each predicted class relative to the total number of observations in the predicted class. The normalized row summary shows the percentages of accurately and inaccurately classified observations for each actual class. Experiments show that vehicle type classifiers encounter the most difficulty distinguishing between buses and trucks, whereas vehicle color classifiers struggle the most, distinguishing between orange and red. The experiment resulted in a 93.9%  
Figure 11: Results of training progress of vehicle type and color classification learner, (**a**) training progress of vehicle type classification learner (**b**) training progress of vehicle color classification learner. accuracy rate for vehicle type classification and a 94.2% accuracy rate for vehicle color classification. Figure 13 shows the results of detecting a moving vehicle from a road image and classifying its type and color. The experimental image on the left in Figure 13b failed to detect the truck on the left, misclassified the SUV in the center as a truck, and misclassified the truck as an SUV. The image on the right shows a vehicle in the opposite lane misclassified as a sedan.