## 4 DL in Addressing Crucial Issues For Target Detection
### One-Stage Detectors in Target Detection
Moreover, with small targets and mixed backgrounds, the position of the bounding box is more precise which is very essential for target detection in UAV images. In 2021, [PERSON] et al. [60] proposed a Traffic Analysis from UAVs (TAU) approach to detect all of the existing targets inside one assembly and generated a UAV image-based \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Issue** & \begin{tabular}{c} **One-Stage Detectors** \\ \end{tabular} & **Measure** & **Prototype** & **Reference** \\ \hline Target Occlusion & YOLOv3 & \begin{tabular}{c} computing cost, \\ processing speed \\ \end{tabular} & \begin{tabular}{c} K-means++ algorithm, \\ Soft-NMS algorithm and \\ data augmentation \\ technique \\ \end{tabular} & [64] \\ \hline Complex Background & YOLO & \begin{tabular}{c} computing cost, resource \\ utilization, computing \\ power \\ \end{tabular} & \begin{tabular}{c} CSP Bot method, data \\ augmentation, and \\ Adaptive Spatial Feature \\ Fusion approach \\ \end{tabular} & [65] \\ \hline Joint Issues & YOLOv4 & cost & \begin{tabular}{c} K-Means Clustering, \\ Data Enhancement \\ Approach \\ \end{tabular} & [66] \\ \hline YOLOv4 & \begin{tabular}{c} computing cost, boost \\ the efficiency, material \\ resources \\ \end{tabular} & \
\begin{tabular}{c} RFB, ULSAM and \\ Soft-NMS design \\ \end{tabular} & [67] \\ \hline YOLOv4 &
\begin{tabular}{c} computing cost, safety \\ and stability \\ \end{tabular} & YOLOv4 & [68] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._dataset which is divided into five groupings such as pedestrian, motorcycle/bicycle, car, truck, and bus. However, to further pursue the detecting target, an online multi-object tracking approach called DeepSORT was utilized [74]. This approach decreases the time consumption, guarantees safety on the highway, and somehow reduces the computing cost. However, YOLOv3 still entails high extraction cost. In addition, in the current genre, the TAU approach has a few limitations which are:  
* The incoherence of the metrics of the x and y axis when the pixel indicator is a multiple of the height and breadth of the rectifying frames. * Due to the high resolution, it is unsatisfactory to pass it online. So, it is necessary to solve these limitations with some new generic algorithms in the future. In 2020, [PERSON] et al. [75] presented a design composed of four segments: (i) vehicle detection, (ii) background registration, (iii) trajectory construction and compensation, and (iv) trajectory denoising to draw remarkably well the orbit of highway users which include pedestrian, motor vehicles (MV) and non-motor vehicles (NMV) such as bicycles, tricycles, and motorcycles and to track the small target trajectories. The YOLOv3 algorithm was applied in the first segment for detection accuracy and to get the target bounding boxes at this stage. To gain the image locomotion in the second step, the Shi-Tomasi corner attribute is utilized. This approach is an extremely popular corner detector and is extensively used due to its high correctness and fast speed for numerous real-time clarification applications and manipulated for monitoring and tracking of the target characteristics [76,77]. Trajectory construction and compensation is the third step of this design which has three main phases: (i) data correlation, (ii) trajectory classification, and (iii) trajectory compensation. The purpose of these phases is to configure the irregular vehicle trajectories formed on the basis of the perception of the speed restraint, contest the smashed trajectories, and implement the assembly tasks to rectify the omitted components. Moreover, the ensemble empirical mode decomposition approach is employed in the last step to remove noise and errors from arbitrary and unbalanced signals and enhance the trajectory reliability [78]. The exploratory outcome reveals that the presented design attains high accuracy in trajectory abstraction and detection.