###### Abstract  
In smart cities, target detection is one of the major issues in order to avoid traffic congestion. It is also one of the key topics for military, traffic, civilian, sports, and numerous other applications. In daily life, target detection is one of the challenging and serious tasks in traffic congestion due to various factors such as background motion, small recipient size, unclear object characteristics, and drastic occlusion. For target examination, unmanned aerial vehicles (UAVs) are becoming an engaging solution due to their mobility, low cost, wide field of view, accessibility of trained manipulators, a low threat to people's lives, and ease to use. Because of these benefits along with good tracking effectiveness and resolution, UAVs have received much attention in transportation technology for tracking and analyzing targets. However, objects in UAV images are usually small, so after a neural estimation, a large quantity of detailed knowledge about the objects may be missed, which results in a deficient performance of actual recognition models. To tackle these issues, many deep learning (DL)-based approaches have been proposed. In this review paper, we study an end-to-end target detection paradigm based on different DL approaches, which includes one-stage and two-stage detectors from UAV images to observe the target in traffic congestion under complex circumstances. Moreover, we also analyze the evaluation work to enhance the accuracy, reduce the computational cost, and optimize the design. Furthermore, we also provided the comparison and differences of various technologies for target detection followed by future research trends. unmanned aerial vehicles; target detection; traffic congestion; deep learning; YOLO versions; faster R-CNN; cascade R-CNN +
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several disadvantages such as fuel consumption, time loss, mental pressure, and aggravates air pollution. Accurate and timely target detection in intelligent transportation networks may reduce traffic congestion. Target detection is utilized to find out where targets are located in the area and which group they are associated with such as pedestrians [1], vehicles [2], etc. Many researchers tried to handle the target detection problem in various fields such as solving the target detection in the form of pedestrians in autonomous vehicles under different sub-issues such as occlusion, large and small-size objects, and complex backgrounds with the use of various strategies [3,4]. However, in this review paper, we study target detection through UAVs. UAVs, which are also called \"Drones\" or \"Unmanned Aircraft Systems\", are an airship that works automatically through remote controls and sensors without any human aviator, crew, or rider. There are four major types of UAVs as shown in Figure 1. Among multi-rotor drones/UAVs, the quadcopter is much more famous due to its approachability and virtuous camera command as compared to other types of drones/UAVs, it is easy to use and can work in a restricted region. UAVs have been integrated in different fields, for example, computer vision [5], search and rescue operations [6], and communication systems [7; 8; 9]. UAVs play an important role in military and non-military applications such as surveillance, filmmaking, attack, cultivation, scientific analysis, cargo transport, and many more due to low cost, mobility, ease to use, and accessibility [10]. The advantages of drones/UAVs lie in rescuing time and investment, boosting the reliability of fact measurement, increasing the security of data logging, improving the performance of complex scenarios, and making the investigation more systematic. Despite all its advantages, UAVs also face several significant challenges in terms of full deployment, which are given below:  
* In terms of heavy mobility, UAVs have insufficient ability to mark traffic motion evaluation at the intersection due to their dimensions, underneath stimulation, and braking capabilities.