## 1 Introduction
Second, learning efficiency is enhanced by skipping layers through residuals, which facilitates information transfer and reduces the gradient loss problem, thereby enabling the training of deeper networks. Third, ResNet-50 provides pre-trained models on large datasets, such as ImageNet. These pre-trained weights can be used to quickly learn the features required for vehicle detection. This has the advantage of being able to train the model effectively, even when the amount of training data is small. Finally, the ResNet-50 CNN has a relatively high accuracy compared with the other models, excellent computational efficiency, and efficient memory usage. These advantages enable the ResNet-50 CNN to achieve high accuracy and efficiency in vehicle detection research, facilitating the fast and accurate detection of vehicles in various environments. Thus, the proposed method establishes a neural network that can perform vehicle detection, type, and color classification relatively accurately in real-time in a relatively small learning data environment. The superiority of the proposed method has been demonstrated through various experiments to optimize the learning parameters. Training the neural network used to extract vehicle features from road driving images requires significant time and effort. The proposed method involves extracting image features using a pre-trained neural network, and then training a classifier with these features. Vehicle features were trained using the YOLO v4 classifier. Using a pre-trained neural network for feature extraction offers a fast and efficient method to harness deep learning capabilities without training the entire neural network from scratch. This method is particularly useful when a GPU is unavailable because it requires only one pass through the training images. The proposed method utilizes a pre-trained image classification neural network to extract features from road images and learn vehicle detection, type, and color classification tasks. Transfer learning, using a pre-trained neural network, is typically faster and easier than training a neural network from scratch. When selecting a pre-trained neural network for vehicle detection, we must consider its accuracy, speed, and size. If the vehicle learning dataset is small, obtaining driving data for all road environments can be challenging. In such cases, we should use transfer learning to extract features by applyinga pre-trained neural network. The advantage of using a pre-trained neural network as a starting point is that it allows for a faster training speed when training a simple classifier on the extracted features. In addition, fine-tuning the deeper layers of a neural network on a new dataset can enable it to learn features specific to that dataset, even if the network has already learned a variety of image features. This approach is particularly useful for training a neural network to recognize vehicles from driving images. Fine-tuning a neural network is a relatively slow and more effort-intensive process than simple feature extraction. However, as a neural network can be trained to extract different features, the final neural network often provides more accurate results. In the proposed method, ResNet-50 is used as an effective feature extractor. ResNet-50 is a convolutional neural network that consists of 50 layers. This pre-trained neural network could classify images into 1000 object categories, including keyboards, pencils, and various animals. It can learn various features representing different images. The feature was used as a classifier. Through various studies, YOLO deep learning networks have been developed from v1 to v8. YOLO v1 and v2 are suitable for models in which accuracy is prioritized. YOLO v3 and v4 are suitable for models in which speed is a priority. YOLO v5 and v6 are suitable for models that require a balance between accuracy and speed.