## 4 DL in Addressing Crucial Issues For Target Detection
### _Two-Stage Detectors in Target Detection_  
This subsection presents relative work on the adoption of two-stage detectors in resolving the issues of target detection through UAVs in traffic congestion. The basic work is summarized in Table 3.  
1. _Two-Stage Detectors for Small-Size Objects:_ [PERSON] et al. [88] presented a faster R-CNN-based approach to detect the small-size target when the quantity of the corresponding anchor is deficient or when the targets are adjacent or lightly overlapped. This is because an insufficient quantity of the corresponding anchor increases the calculation complexity of the network. Therefore, the proposed model is divided into five phases: 1. As a feature extractor, ResNet101 is utilized to reduce the incline dispersion issue while reinforcing the system depth [97]. 2. The RPN is demonstrated to produce the anchors of different sizes and dimensions. 3. By attaining the feature interconnection and cross-channel integration and minimizing the feature portray channels, a convolution layer of \(1\times 1\) is used. 4. The RoIAlign algorithm is used to prevent the loss of margin pixels. These pixels help to track and differentiate between small targets and adjacent or lightly overlapped targets. It also determines the misconfiguration generated by the RoIPool. 5. To analyze the targets and environmental situations through the image domain and purify the target domain bounding boxes, the classification and regression system is utilized. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Issue** & **Two-Stage Detectors** & **Measure** & **Prototype** & **Reference** \\ \hline \hline \multirow{3}{*}{Small-Size Objects} & Faster R-CNN & Computing complexity, computing cost & cross-channel attributes, RoIAlign algorithm, loss function and data augmentation approach & [88] \\ \cline{2-5}  & Faster R-CNN & processing efficiency, computing cost & GC-faster-RCNN, cluster approach, Resnext50 channel attention & [89] \\ \cline{2-5}  & Faster R-CNN & Processing efficiency, computing cost & GC-faster-RCNN, cluster approach, Resnext50 channel attention & [99] \\ \cline{2-5}  & Faster R-CNN & Computing complexity & Utilized dataset of wide-range “object detection in Aerial images (DOTA)” and Annotation framework & [90] \\ \cline{2-5}  & Cascade R-CNN & Computing time and cost & ECascade R-CNN network & [91] \\ \hline \multirow{2}{*}{Target Occlusion} & Faster R-CNN & computing cost, processing speed & RPN and Multi-layer feature fusion & [92] \\ \hline \multirow{3}{*}{Complex Background} & Faster R-CNN & Computing cost and complexity, & HOG+SVM, Faster R-CNN, Vibe and YOLOv3 & [93] \\ \hline \multirow{3}{*}{Joint Issues} & Faster R-CNN & computing cost and complexity & MS-Faster R-CNN and DeepSORT & [94] \\ \cline{2-5}  & Cascade R-CNN & computing cost, efficiency, resource utilization & superclass detection design, Regression Function Improvements & [95] \\ \cline{2-5}  & Cascade R-CNN & computational efficiency, safety and privacy & Faster R-CNN design, SSD and Cascade R-CNN + FPN & [96] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Two-stage detectors to tackle the issues in target detection. Experimental evaluation was performed on the COCO dataset, where image flick augmentation is deployed in a horizontal position to achieve a model accuracy of 79.77%. To detect small-size objects according to different weather conditions, considered complex spots, highways, and roads, in 2022, [PERSON] et al. [89] presented a GC-faster-RCNN where GC is called \"Group Convolution\" which is gained by boosting the Faster R-CNN algorithm and various models. This includes a cluster approach to examine the datasets and, in the replacement of real feature extraction, the lattice Resnext50 is employed. Moreover, to enhance the statistics of features depicting into the network, an output attention mechanism of the enhanced channel is unified. During the testing, it was observed that the detection accuracy slightly increased by 94.8% while the speed of detection was slow.