## 1 Introduction  
The Human Vision System (HVS) reliably and accurately performs complex tasks, such as being able to detect and recognize and identify diverse range of objects with little conscious attention. With the recent developments in the Computer Vision (CV) and Machine Learning (ML), and with the availability of capabilities, such as massive data sets, faster GPUs, and better algorithms, it has now become possible for computers to detect, recognize, and classify several items in an image or video with high accuracy [1]. The aim of vehicle detection and classification is to locate vehicles in either images or videos [2]. Efficiency of vehicle localization is a critical step in traffic monitoring or surveillance. Figure 1 shows several detected vehicles from Pakistani traffic images that are achieved using the machine learning algorithms. Therefore, autonomous vehicle detection methods must exactly detect traffic objects, such as cars, vehicles, or police vans or bikes in real-time to gain good control and make right decisions for the public safety [3]. With the development of the DNNs, automatic vehicle detection has made substantial progress in recent years, for instance, in Autonomous Driving Systems (ADS) and driver support systems in the context of concerns about traffic congestion and driving safety [4]. To develop intelligent and autonomous systems, for instance, self-directed driving, surveillance, detecting objects, or tracking, vehicle localization is a crucial problem [5]. Automatic driving is a new high technology invention that relies on the ability to only find vehicles [6]. In the metropolitan areas, frequent incidents happen regarding traffic breaches, vehicle mishaps, and thefts that are recorded through the CCTV cameras. Traffic surveillance system detector should be fast, accurate, and reliable enough to detect vehicles in real-time. In the areas of traffic managing systems or surveillance technology, there have been numerous advancements. Two essential conditions are normally considered to rate vehicle detectors, which are its real-time detection ability and whether it has a high detection accuracy of the traffic objects under adverse weather conditions. One of the interesting efforts to locate vehicles is to detect abnormalities in traffic violations, as well as careless driving on the roads. With the introduction of new technologies in the ITS and the growing demand for automation, the employment of technology in a variety of disciplines has become inevitable. Because of the growing number of cars on the road, automated vehicle traffic monitoring is one of the most important applications being developed for speed or traffic control, offence detection, road tolls, and a variety of other related issues. To manage such issues, large amounts of general budget is consumed. In large and congested cities, traffic surveillance is a major challenge. ITS mobility planning traffic engineering applications have made significant progress in reducing city incidents. Surveillance systems nowadays use traffic flow data, which typically consider crucial factors, such as speed, size, trajectory, and vehicle type. Moreover, vision-based systems are also used nowadays to monitor and record various traffic patterns. Due to the developments in the DNNs, the ML based models can be reliably used to detect various vehicles, although the training speed of deep learning networks is much slower in the CPU calculations. However, the training time is significantly much less thanks to developing technologies, such as GPUs and the TPUs. When compared to standard ML-based approaches, the DNNs have significantly enhanced performance in various scenarios, such as smart self-governing, self-driving vehicles, intelligent observations, and smart city-based applications. DNNs that are based on neural networks constitute an advanced category of machine learning, which is very handy at resolving difficulties in a variety of complex models that usually are hard to explain through typical statistical techniques.