## 1 Introduction
Moreover, the CNN, which is a form of deep neural network, is extensively used for image recognition and categorization. These are the algorithms that can identify various objects, such as, license plates, cars, people, and a variety of other objects [5]. A primary benefit of the CNN is that it extracts essential features without any human interaction after the training process. Different versions of the CNN, such as R-CNN, Fast-RCNN, and Faster-RCNN are the most popular and commonly utilized CNN approaches [5]. However, the computational load is still too high for devices with limited computing power and space to process photos. The D-based algorithms have long been regarded as effective tools for image recognition. The CNN-based methods have been frequently used in recent approaches among the many vehicle detection algorithms and are divided into region-based and regression-based methods. Figure 1: Vehicle detection examples from Pakistani traffic. The YOLO is a new method to detect diverse vehicles in a single step. The YOLO handles the vehicle perception problem as a regression problem by classifying the image via CNN, which is utilized to achieve robust vehicle detection. The YOLO can retrieve the object's position, category, and confidence score, as well as boost detection speed and detect the motion-blurred vehicles in real-time. A regression-based YOLO technique is one of the most current ways to predict bounding boxes and class probabilities directly in a single neural network. As a result, the YOLO model was created to speed up the process to identify an object and find its location in an image. It uses the CNN to detect several items in an image immediately. To handle vehicles of different shapes and sizes, it integrates predictions from many feature maps with different resolutions. With advancements in the YOLO based methods, such as YOLOv3 and YOLOv5, the YOLO continues to provide higher performance in terms of processing time and accuracy [3]. In this work, we target the detection and classification of vehicles in images using deep learning to explore the feasibility of YOLO based methods. The YOLO family of algorithms is first-order object detection method, which uses an anchor box to integrate various objects localization. Up to now, five versions of YOLO family of algorithms have been released. The YOLOv3 is a milestone in the performance and speed of the YOLO family of algorithms. Our motivation to choose the YOLOv5 detection model is due to its smaller architecture and much fast detection ability than the previous generations of its model families. Recently, researchers in various research domains have enhanced the original YOLOv5 model based on the characteristics of their detection targets, which makes the YOLOv5 algorithm an excellent choice in vehicle detection domain. Our main contributions in this work are listed below. * We propose a modified version of the YOLO algorithm to achieve vehicle detection in real time. Earlier-developed works have been trained on massive datasets, but still need to be fine-tuned for use in congested traffic environments. However, we augment these datasets with our gathered datasets. We compare the efficiency of our trained version with several recent state-of-the-art methods. * We detect and classify vehicles in images that are captured in various traffic scenes. We perform detailed study on the PKU, COCO, and DAWN datasets. To achieve higher accuracy on images from our local traffic patterns, we gathered an extensive dataset and applied transfer learning to the YOLOv5. The input to a system is a real-time image, and the output is a bounding box corresponding to all objects in the image, along with the class of object in each box. * In addition, we employ a transfer learning approach to utilize the knowledge embedded in our local datasets. We believe that the ITS based applications require rapid and precise vehicle identification and classification.