## 4 DL in Addressing Crucial Issues For Target Detection  
### One-Stage Detectors in Target Detection  
In this subsection, we review the useful work of one-stage detectors in resolving the issues of target detection through UAVs in traffic congestion, which are outlined in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Issue** & \
\begin{tabular}{c} **One-Stage Detectors** \\ \end{tabular} & **Measure** & **Prototype** & **Reference** \\ \hline \multirow{3}{*}{Small-Size Objects} & \multirow{3}{*}{YOLO} & computing cost, time complexity, low efficiency, high computing resources & Fusion of DL and traditional model detection & [58] \\ \cline{2-5}  & & computing cost, resource utilization & YOLO-GCC and Traffic-DQN & [59] \\ \hline \multirow{3}{*}{YOLOv3} & computing cost, safety and privacy, resource utilization & TAU and DeepSORT & [60] \\ \cline{2-5}  & & Resource usage and time complexity & Tiny YOLOv3 & [61] \\ \cline{1-1} \cline{2-5}  & & computing cost, computing power &
\begin{tabular}{c} SiamMask target tracking technique \\ \end{tabular} & [62] \\ \hline \multirow{3}{*}{YOLOv4} & \multirow{3}{*}{computing cost} & KCF tracking method and average peak-to-correlation energy scheme & [63] \\ \cline{1-1} \cline{2-5}  & & & \\ \cline{1-1} \cline{2-5}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: One-stage detectors to tackle the issues in target detection. 1. _One-Stage Detectors for Small-Size Objects_: In 2016, YOLO [18] performed real-time target detection to simultaneously predict the probability of location reliability and all target classes. In 2021, [PERSON] et al. [69] proposed the YOLO approach because this approach is easy and simple. The target detection issue has been completely resolved by regression. The images captured from the UAVs have high-resolution characteristics. For the target, recognition detection used the VGG16 network as a backbone formation and the optimization scheme utilized the adaptive moment estimation Adam [58] in the training process which aims to hasten the speed of the prototype convergence. When data were deficient in an inadequate feature extraction network, they introduced transfer learning to enhance the accuracy of the training recognition estimation. Moreover, YOLO merges the target locality forecast and grouping forecast into an isolated neural network prototype to attain quick target recognition and detection with high reliability. In the target detection, the detection rate of the YOLO network is extremely high with 69% and the detection speed is 40 FPS/s, apart from the detection reliability which is lower as compared to other DL networks. [PERSON] et al. [59] proposed a global context cross (YOLO-GCC) model which signifies the design of YOLOv3 and GCNet to handle the blurring and fuzzy features of small objects. To extract several multi-dimensional feature maps, this model utilized the DarkNet-53 [21] as a backbone. The global context attention segment was attached as the latest backbone with DarkNet-53 and called GC-DarkNet to extract further accurate and compelling features. The H-Swish activation function was used to decrease the computing cost. In addition, an approach of intelligent traffic signal planning called Traffic Deep-Q Network (Traffic-DQN) is introduced which is based on deep reinforcement learning, taking the advantage of traffic flow facts gained from the YOLO-GCC and is used as the basis for transportation planning. The Traffic-DQN system shows apparent benefit in convergence velocity, and each diagnostic indicator is better than the corresponding one in the other approaches. The experiment testing was performed on four familiar UAVs datasets: (i) the UCAS-AOD dataset [70], (ii) the VisDrone2019 dataset [71], (iii) the TSD-MAX dataset [72], and (iv) the UA-DETRAC dataset [73] which comprises different classes such as car, bus, van, and others. The exploratory results show that the potential of the proposed method to identify small flow factors is clear and it is better than the YOLOv3 algorithm.