## 3 Proposed Vehicle Detection and Tracking Scheme
### YOLOv5 Model Overview  
The most effective object identification model was created by 58 open source contributors over the course of continuous development, and it is called YOLOv5 [55] to achieve a higher detection accuracy and a higher speed than all the earlier versions of the YOLO model. There are several improvements with this model compared to the other YOLO models. First, it supports PyTorch than [PERSON]'s Darknet used in the previous version of this model. The configuration of the Darknet with an earlier variant of YOLO is much more challenging and less productive due to the smaller community of users. In YOLOv5, the support and deployment are much easier. Second, compared with YOLOv4 50 Frames per second, it can achieve 140 frames per second (FPS) [36]. The foremost advantage of selecting the YOLOv5 model for real-time vehicle detection is its superior detection accuracy compared to all the previous versions of the YOLO model. [54]. Fourth, the size of the weight file is much smaller in YOLOv5 (27 megabytes) compared to the YOLOv4 weight file (244 megabytes), which is approximately 90 percent bigger than YOLOv5. YOLOv5 mainly uses three basic components, namely YOLOv5 Backbone, YOLOv5 Neck, and YOLOv5 Head. YOLOv5 Backbone uses the CSP Darknet as the foundation for obtaining features from images formed up of cross-stage partial networks. When YOLOv5 Neck aggregates features using PANet, it creates a feature pyramids network and sends it to the head for prediction. For object detection, the YOLOv5 head predicts based on the anchor boxes. YOLOv4 and YOLOv5 implement the CSP Bottleneck to develop image features for the CNN backbone [55]. The CSP has addressed the same gradient problems in other more extensive convolutional neural network (ConvNet) backbones, resulting in fewer parameters [36] and fewer FLOPS for practically identical significance. For example, DenseNet uses the CSP model to connect layers in convolutional neural networks to bolster the feature propagation and alleviate the vanishing gradient problem. The YOLOv5 additionally utilizes the leaky ReLU and Sigmoid for activation. It also makes use of SGD and ADAM to perform optimization.  
In autonomous vehicles, object detection is crucial to identify and localize objects within a scene, thereby enabling the detection and tracking of their presence and location. The CNN-based high-speed object detection method YOLOv5 leverages a regression problem to provide class probabilities [36]. It has since been revised in certain ways. One forward propagation pass through the CNN is all that is needed for YOLO to make predictions. Following that, it produces objects with the corresponding bounding boxes. It is frequently used in autonomous vehicles to find items in the environment. The primary goal of object detection is to identify an object's class and determine its location using the bounding box. Therefore, the YOLOv5 model is selected to detect vehicles due to its high accuracy, performance, and the small size of weight file. First, the bounding box image is obtained by applying the YOLOv5 model [36] on an input image. Then, at that point, it denotes the vehicle's location for that frame, stores the location information, and assigns the detection ID. Then, the detection result is fed into the Deep SORT to track the vehicle's location. The methodological flow of the YOLOv5-based model is illustrated in Figure 2.