## 2 Target Detection Prototypes and Their Related Metrics and Crucial Issues
### Target Detection Prototypes  
The dominant features of UAVs in target detection are elaborated in Table 1.  
_Mobile UAV Trajectories Based on Road Traffic Monitoring Approach:_ Within a town, due to the insufficient amount of UAVs, it is not appropriate to mark a comprehensive tracking of all the targets in terms of vehicles, pedestrians, and motorbikes. To handle this issue, a mobile UAV-based road traffic monitoring system was proposed by [PERSON] et al. [33] in 2019.  
Advantages: This mode aims to track the detection rate and the statistics of restrained vehicles to overcome accidents and overspeeding. Moreover, it initiates UAV trajectories to observe targets within a town from a long distance to avoid traffic congestion.  
Limitation: It is necessary to enhance the detection rate of an isolated vehicle of a congested event at a low speed by sharing the facts related to dispersion through UAVs.  
_DeepSort Approach:_ For electric vehicles and target detection in metropolitan surroundings, a DeepSORT approach based on DL has been formulated by [PERSON] and [PERSON] [34] in 2021. This approach combines YOLOv4, various tracking methods, and fuses the target detection web to reduce the state estimation of the pursued target in the non-uniform tactic and realized the target position through UAVs.  
Advantages: The combination of the presented model aims to significantly enhance the performance, robustness, and positioning of numerous targets' perception and tracking in complicated metropolitan surroundings.  
Limitation: Still, the performance is deficient when the UAVs are fluttering at a high elevation, which may cause the problem in detecting the tiny bulk of the ground substance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Merit** \\ \hline Human Workload & Moderate \\ \hline Financial Resources & Prevent \\ \hline Mobility & High \\ \hline Efficiency & High \\ \hline Battery Timing & Low \\ \hline Service Access & Multiple Access \\ \hline Validity & High \\ \hline Speed of Detection & High \\ \hline Frequency & Low \\ \hline Adaptability & Centralized \\ \hline Safety & Less Secure \\ \hline Resolution & High \\ \hline Application Range & Vast \\ \hline Transportability & Flexible \\ \hline Mobility & High/Strong \\ \hline \hline \end{tabular}
\end{table}
Table 1: UAV features in target detection.  
3. _SAHER System Approach Based on UAVs_: In traffic congestion, road misadventures are caused due to energy and coverage issues. To handle this issue, [PERSON] et al. [35] proposed a SAHER system based on UAVs using the 5G data processing in 2020. * Advantages: In real-time scenarios, this approach detects swiftness and alternative traffic interruptions to overcome the number of crashes. * Limitation: The proportion of tragedies and wounds is still extremely high.
4. _Traditional and UAVs Vision Approach:_ In 2022, [PERSON] et al. [36] presented a model based on traditional and UAV approaches which mainly include YOLO 3, Mean-Shift, Gaussian background difference approach, and Kalman filter algorithms to observe the unauthorized behavior of the target. * Advantages: The aim of this approach was to compare the results of UAVs and traditional approaches on the four features: manual time, divergence results, recognition speed, and accuracy. Therein, the target detection based on the UAV approach performs better as compared to the traditional approach. This is because, in the traditional approach, the computational cost is low and poor in robustness. Furthermore, it cannot fulfill the substantial application demands in real-time detection. The comparison results of UAVs and traditional approaches based on four features are demonstrated in Figure 4. * Limitation: Still, some conflicts arise. If the target detection results are not enough, then it will create errors in the detection outcomes and may affect future observations.