## 3 Experimental Results
### Evaluation of Vehicle Type and Color Classification Accuracy  
The bounding box for the detected vehicle was trained using the Resnet-50 CNN for vehicle type and color classification. Augmented data was generated from training images using various methods for vehicle type and color classification. The ResNet-50 CNN, a trained model, is designed to classify 1000 different objects. To classify vehicle type and color, the last fully connected layer, softmax layer, and classification layer of the Resnet-50 CNN were removed, and a new output layer was connected. For the transfer learning of the vehicle type classification, the output size of the new fully connected layer was set to 4, which is the vehicle type to be classified, the Weigh Learning Rate Factor for the weight was 10, the Bias Learn Rate Factor for the bias was 10, and the learning rate factor for the bias was 10, and the L2 normalization factor for the bias has been was set to 1. To generate the augmented data, random changes were applied to the x- and y-axes. Rotation is in the range of \(-\)10 to 10 degrees, scaling is in the range of 0.5 to 1.5, and horizontal and vertical pixel movement is in the range of \(-\)5 to 5. Color changes were made randomly within 10% of the Hue, Saturation, and Intensity values in the HSI color space. The dataset used to train the vehicle type classifier consists of 26,125 images, whereas the dataset used to train the vehicle color classifier consists of 115,356 images. The dataset for vehicle classification uses only the rear parts of the vehicle while driving. The dataset for vehicle color classification, however, uses various images, including the front, side, and rear parts of the vehicle, acquired under different illumination conditions to improve color classification accuracy. The total learning dataset was divided into a training dataset (60%), a verification dataset (10%), and a test dataset (30%). Figure 7 shows the training dataset used for vehicle type and color classification. Table 3 presents the results of the accuracy experiments for various learning parameters \begin{table}
\begin{tabular}{c c c c c c} \hline
**Input Layer Size** & 227 \(\times\) 227 & 224 \(\times\) 224 & 224 \(\times\) 224 & 224 \(\times\) 224 & 224 \(\times\) 224 \\ \hline \multirow{4}{*}{Feature Layers and Size} & \multirow{4}{*}{\begin{tabular}{c} \(\text{relu\_conv10}\) [14 \(\times\) \\ 14 \(\times\) 1000] \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} inception\_5b- \\ output [7 \(\times\) 7 \(\times\) \\ 1024] \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} \(\text{res5b\_relu}\) [7 \(\times\) 7 \(\times\) \\ 512] \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} out\_relu [7 \(\times\) 7 \(\times\) \\ 1280] \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} activation\_22\_relu [7 \(\times\) 7 \(\times\) \\ \(\times\) 7 2048] \\ \end{tabular} } \\  & \(\times\) 3 [28 \(\times\) 28 \(\times\) 128] & & & \(\times\) 256] \\  & & 832] & & & \(\times\) 14 \(\times\) 576] \\ \#1 & 76.12 & 69.86 & 84.30 & 82.07 & 89.01 \\ \#2 & 71.33 & 74.45 & 85.12 & 85.81 & 93.30 \\ \#3 & 68.50 & 72.63 & 81.67 & 82.34 & 89.18 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of vehicle detection accuracy by pre-trained DNNs applied for feature extraction on the Head part of the YOLO v4 vehicle detector structure. used in the vehicle type and color classifier. To select optimal learning parameters for the classification accuracy experiments, the proposed method generates a set of options for training neural networks using Stochastic Gradient Descent with SGDM Momentum, Adam optimization function and RMSProp optimization function. The learning rate was reduced by a factor of 0.2 every 5 epochs. The maximum number of epochs is 30. The mini-batch size used for each training iteration was either 64 or 128. The initial learning rates for training were set to 0.0001 and 0.00001.