## 3 Experimental Results
### Evaluation of Vehicle Detection Accuracy According to Hyperparameters and Training Options  
To evaluate the performance of the proposed method, the training dataset was divided into 60%, 10%, and 30% for training, validation, and testing, respectively, and the size of the input image was normalized to 224 \(\times\) 224. Experiments were conducted to determine the obtain optimal training options and neural network hyperparameters for the YOLO v4 DNN learning process for vehicle detection, as shown in Table 1. To order to select the optimal parameters for training a deep learning NN, training was performed by setting the NN solver was to [Stochastic Gradient Descent with Momentum (SGDM) [38], Adaptive Moment Estimation (Adam) function [39], Root Mean Square Propagation (RMSProp) function [40], the initial learning rate to {0.00001, 0.0001, 0.001}, the mini-batch size to {32, 64, 128}, and the epochs to 30. Among the NN training options, the experiment was conducted with Reset Input Normalization set to false, Batch Normalization Statistics set to moving, and Execution Environment set to GPU for the hardware resource to be used for NN training. The values of Weight Learn Rate Factor and Bias Learn Rate Factor of the fully connected layer were both set to 10 to allow faster learning in the new layer than in the transferred layer of the ResNet-50 CNN. Table 1 shows only the top 10 experimental scenarios with the highest average vehicle detection accuracy out of a total of 27 scenarios. Table 1 shows that the NN learning solver used the RMSProp optimization function, resulting in the highest accuracy for vehicle detection, and the Square Gradient Decay Factor was set to 0.9. The initial learning rate used for NN training was 0.0001, and the maximum number of epochs trained was 30. The mini-batch size used for each training iteration was set to 32 to achieve the highest vehicle detection accuracy and the lowest detection failure rate. Figure 9 shows the Precision-Recall (PR) curve, which represents the average accuracy of the vehicle detection results in road images, and the log miss rate-false positives image (FPPI)-based log error rate from the vehicle detector generated using the optimal learning parameters for vehicle detection. Precision refers to the ability of the vehicle detector to perform the correct classification (Precision = TP/(TP + FP)), and Recall refers to the ability to identify all related objects (Recall = TP/(TP + FN)). The PR curve shows the precision of the vehicle detector at various recall levels. Ideally, the precision should be 1 at all recall levels. Increasing the size of the training dataset can improve the average precision, but this approach has limitations that require additional training time. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Solver**} & **Initial Learn** & **Mini-Batch** & **Average** & **Average** \\  & & **Rate** & **Size** & **Miss Loss** & **Precision** \\ \hline
1 & rmsprop & 0.0001 & 32 & 0.1577 & 91.20 \\
2 & rmsprop & 0.0001 & 128 & 0.1878 & 90.51 \\
3 & rmsprop & 0.0001 & 64 & 0.2092 & 89.14 \\
4 & adam & 0.0001 & 32 & 0.2585 & 83.39 \\
5 & rmsprop & 0.001 & 32 & 0.2474 & 82.01 \\
6 & sgdm & 0.0001 & 32 & 0.2875 & 81.55 \\
7 & sgdm & 0.0001 & 64 & 0.3310 & 79.74 \\
8 & rmsprop & 0.00001 & 32 & 0.3362 & 74.83 \\
9 & sgdm & 0.0001 & 128 & 0.4175 & 70.22 \\
10 & rmsprop & 0.00001 & 64 & 0.4858 & 60.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the top 10 vehicle detection accuracy by different hyperparameters and training options in the YOLO v4 vehicle detector training scenario. Figure 10 shows the result of detecting a vehicle in front in a road image with the vehicle detector with the highest average detection accuracy (AP) among different hyper-parameters. The experiment resulted in an average vehicle detection rate of 91%. During the experiment, errors in vehicle detection occurred due to misidentification of the vehicle in the opposite lane.