## 3 Experimental Results
### Evaluation of Vehicle Detection Accuracy by Applying Transfer Learning Networks  
Table 2 shows a performance comparison table of the vehicle detection results obtained using various transfer learning networks. The vehicle detector was trained by extracting features from the activation layers of the base network on the detection Head part of the YOLO v4 structure. To extract features from the input image, specific feature extraction layers configured in the base network were added to the YOLO v4 detection head. The pre-trained deep learning network used as the base network applies transfer learning to the YOLO v4 vehicle detector for feature extraction performance. To compare vehicle detection accuracy, the experiment was conducted using various transfer learning networks including SqueezeNet, GoogLeNet, Resnet-18, Resnet-50, and MobilNet-v2 [41]. The vehicle detector was trained using the RMSProp optimization function with a learning rate that was reduced by 0.2 every 5 epochs. The maximum number of training epochs was set to 30, with a mini-batch size of 64 for each iteration. The initial learning rate used for training was 0.0001. The transfer learning NN of the YOLO v4 network extracts features from the input image by selecting the activated layer from the last convolution layer and the first convolution layer, where the size of the input vector is reduced by half.  
Figure 9: Results of average precision and average miss loss rate of vehicle detector. (**a**) average precision based on Precision-Recall Curve (**b**) the log average miss rate based on false positives per image (FPPI).  
Table 2 presents the experimental results, which show that the activation layers of the Resnet-50 CNN transfer learning network, used as the feature extraction layer in the Head part of the YOLO v4 vehicle detector network structure, achieved the highest vehicle detection accuracy. When comparing the accuracy of vehicle detection using different transfer learning neural networks, it is more effective to use a transfer learning NN for feature extraction and then fine-tune the NN weights through learning, rather than training with randomly initialized weights. This is because features can be quickly transferred to new tasks using fewer training images. This paper extracted features using pre-trained  
Figure 10: Detection result of the vehicle driving in a road driving environment. (**a**) correct detection results, (**b**) incorrect detection results.  
activation layers of the ResNet-50 CNN and detected vehicles using the YOLO v4 deep learning neural network.  
To verify that the bounding boxes detected in the vehicle detection step contain actual vehicles, if the similarity between the detected bounding box and the template generated from the vehicle regions labeled by the ground truth method in the learning step is greater than 0.5. If it is, the bounding box is considered to contain the vehicle.  
The proposed method resulted in an average vehicle detection rate of 91.5%. The low vehicle detection performance is due to the fact that the front area of a vehicle driving on the opposite lane on the highway is detected as a vehicle. This paper only learned to detect the rear area of a driving vehicle, resulting in a low average vehicle detection rate. However, the detection results of over 94% on average were presented in the rear area of the vehicle.