###### Abstract
* In dense urban regions, UAV delivery can be impractical due to a limited utmost payload (mass and volume), comparably low-range, insufficient low-elevation airspace, insubstantial sensor's ability, and limited battery capacity, which can cause difficulties and security risks. Figure 1: Types of UAVs/drones. It has insufficient auto-navigation expertise, transmission, and energy problems to detect different kinds of disasters such as floods, fire situations, and air tragedies. It has insufficient facts about patrolling regions and is unable to examine and track the informant of pollution. * Moreover, target detection in the form of pedestrians, vehicles, and traffic signs is one of the critical issues of UAVs in traffic congestion due to complicated environments, small and heavy recipient size, drastic occlusion, weather and lightning variations, and unclear object characteristics such as appearance and color as demonstrated in Figure 2.  
To overcome the above-mentioned issues in UAV-based target detection, researchers have proposed many DL-based approaches. Recent DL approaches in the area of target detection undergoing UAVs have progressed rapidly by virtue of enormous performance. DL is the class of artificial intelligence and machine learning and has the expertise to gather, learn, and analyze a massive quantity of data. DL uses both graph and transfiguration techniques to create multi-level learning models. In this article, we review the DL approach to address problems of target detection in traffic congestion. In the recent years, many researchers have tried to sort out this issue with the help of different approaches. To handle the tracking and detection issues of the moving target, there have been several tasks based on UAV-initiated cameras using traditional approaches [11; 12]. In 2016, [PERSON] et al. [13] presented the pixel-based adaptive segmenter algorithm for target detection. In [14; 15], fast fourier transform and kernel function were employed on a discriminative correlation filter-based detector to perform the complex computation in the frequency domain rather than in the spatial domain which optimized and enhanced the performance of the detecting model. In 2018, the Kanade-Lucas-Tomasi tracking approach was presented by [PERSON] et al. for target detection tracking [16]. However, traditional approaches are less precise due to bad generalization accomplishment. For vigorous tracking achievement, DeepSort was used which further omitted the DL properties and Kalman filter [17]. Apart from that, in image depth feature screening, CNN unifies the feature abstraction, selection, and categorization which are superior than traditional techniques. Its effectiveness is higher, so the scalability and correctness of the target detection are also much better than traditional techniques. Figure 2: Typical challenges of UAVs with basic framework of target detection in traffic jams through UAV. Furthermore, different object detection approaches have been presented based on CNN, for instance, Region-based Convolutional Neural Network (RCNN) and Single Shot MultiBox Detector (SSD) to perceive the small targets and perform at low elevations. However, their performances are still not good enough in target detection in terms of accuracy [18; 19; 20]. In contrast, for better performance, the single-stage detector techniques such as YOLO v3 and YOLO v4 were presented by [PERSON] and [PERSON] [21] and [PERSON] et al. [22], which are better in target detection and permit excellent real-time achievement and high precision. But it prerequisites a notable computational scheme. Besides this, in [21; 23; 24], UAV-based detection and tracking methods have been presented, which merge the illusion, KCF (kernel correlation filter) based detectors, and YOLOv3. Moreover, many review books and articles have been found to detect targets in traffic congestion. For example, [PERSON] [25] presented a review on the applications of UAVs associated with traffic attention. [PERSON] et al.