## 2 Proposed Method
### Vehicle Detection
As the network progresses through deeper convolutional layers, it becomes capable of detecting more complex and intricate features of the target class. The proposed method inputs images into a pre-trained ResNet-50 CNN to detect vehicles in  
Figure 4: Graph of the average IOU change according to the distribution of the aspect ratio of the bounding box and the number of anchor boxes. (**a**) Box area vs. aspect ratio of vehicle bounding boxes in a road image, (**b**) Number of anchors vs. mean IoU. road images. The output of the ReLU layer of the neural network is then checked, and the convolutional layers that extract the most optimal features are selected from the 50 layers of convolutional layers. Figure 5 shows the activated output value of the first convolutional layer [\(112\times 112\times 64\)] of the Resnet-50 CNN on the road image. The output represents 24 channels out of a total of 64 channels in the first ReLU layer. In Figure 5, white pixels indicate strong positive activation, while black pixels indicate strong negative activation. The network appears to be learning low-level features, such as edges and textures. The first channel emphasizes the features of the vehicle in the input road image. This may be due to the distinct shape, form, and color of the vehicle. Figure 6 shows a visualization of the strongest activation channel in the 40 th ReLU layer [\(14\times 14\times 1024\)]. Additionally, Figure 6 shows the degree of positive activation in the regions where the vehicles are located in the input road image, indicating that the feature extraction layers are extracting more complex features. The activation_40_ReLU layer contains 1024 channels, and an image exists for each channel. However, the visualization results show that only the area containing the vehicle in the input road image is well activated, as confirmed by the channel with the strongest activation. An importance map was computed by taking the derivative of the reduction layer output for the vehicle class from the feature map extracted from the ResNet-50 network convolution layer in the road image shown in Figure 6. In Figure 6, the importance map shows the area containing the vehicle. The dark blue area represents the lowest probability of including the vehicle, while the dark red area represents the highest probability of including the vehicle. The road image shows that the outline and window of the vehicle have the greatest influence on the vehicle detection. Figure 3 shows that feature maps are extracted from the input image and used as input in the Neck part. The SPP module in the Neck part extracts the most representative features from the input image by connecting the maximum pooling output of the low resolution feature map. The SPP module uses a kernel of sizes \(1\times 1\), \(5\times 5\), \(9\times 9\), and \(13\times 13\) for maximum pooling operations, with a stride value of 1. By connecting the feature maps, it is possible to detect small objects, thereby increasing the accuracy of object detection. The feature map connected in the SPP module is merged with the high-resolution feature map using the PAN. The PAN module combines low-level and high-level features using up-sampling and down-sampling operations [8; 37] and outputs a feature map set for object detection prediction. The YOLO v4 network consists of three object detection Heads, each using a YOLO v3 network to compute the final prediction. The network produces feature maps of sizes \(19\times 19\), \(38\times 38\), and \(76\times 76\), and predicts both vehicle bounding boxes and classification scores. Figure 5: Results of activating 24 output channels of the first convolution result using Resnet-50 CNN.