## 8 Results and Discussion of Reviewed Papers
### Au-Air  
In their study, [PERSON] et al. [69] selected AU-AIR as a benchmark dataset to create their proposed real-time object detection model, RSSD-TA-LSTM-GID, specifically designed for small-scale object detection. The performance of their model surpassed that of YOLOv4 [111] and YOLOv3 [33]. The researchers employed the MobileNetv-SSDLite ensemble approach, which yielded the lowest mean average precision (mAP) score.  
[PERSON] et al. [71] employed baseline models on the AU-AIR dataset as one of their evaluative benchmarks. The objective of the study was to demonstrate the attainability of different techniques and ensemble techniques in the detection of objects with varying scales. The baseline technique yielded the highest performance, with a mean average precision (mAP) score of 6.63%. This outcome was achieved by employing color-augmentation on the dataset. The performance metrics for the ensemble methods YOLO+RetinaNet and RetinaNet+SSD were found to be 3.69% and 4.03%, respectively. The authors, [PERSON] et al. [70], made modifications to the architecture of the CenterNet model by using other Convolu  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Name** & **Reference** & **Methods** & **Performance** \\ \hline \multirow{8}{*}{[99]} & & UNetFormer & mIoU (\%) \\  & & 67.8 \\ \cline{2-4}  & & ABCNet & mIoU (\%) \\  & & 63.8 \\ \cline{2-4}  & & BANet & mIoU (\%) \\  & & 64.6 \\ \cline{2-4}  & & BoTNet & mIoU (\%) \\  & & 63.2 \\ \hline \multirow{8}{*}{[100]} & & MSD benchmark & mIoU (\%) \\  & & 57.0 & 1.00 \\ \cline{2-4}  & & BiSeNet [101] & mIoU (\%) \\  & & 61.5 & 11.08 \\ \cline{2-4}  & & CAN & mIoU (\%) \\  & & 63.5 & 15.14 \\ \hline \multirow{8}{*}{[102]} & & FDA-Net [14] & mAP (small) & mAP (medium) \\  & & 0.351 & 0.298 & 0.228 \\ \cline{2-4}  & & PVEN & mAP (small) & mAP (medium) \\  & & 0.825 & 0.77 & 0.697 \\ \hline \multirow{8}{*}{[103]} & & MLSL [104] & mAP (large) \\  & & 0.366 & 0.775 \\ \cline{2-4}  & & FastReID & mAP (large) \\  & & 0.773 & 0.925 \\ \hline \multirow{8}{*}{VERI-Wild [14]} & & GiT & mAP (T10000) \\  & & 0.675 & 0.854 \\ \cline{2-4}  & & PCRNet [106] & mAP (T10000) \\  & & 0.671 & 0.85 \\ \hline \multirow{8}{*}{[107]} & & HPGN & mAP (T10000) \\  & & 0.65 & 0.8268 \\ \cline{2-4}  & & Triplet Embedding [108] & mAP (T10000) \\  & & 0.516 & 0.699 \\ \hline \multirow{8}{*}{[109]} & & Baseline [110] & mAP (large) \\  & & 0.65 & 0.95 \\ \cline{1-1} \cline{2-4}  & & SAVER & mAP (large) \\ \cline{1-1} \cline{2-4}  & & 0.677 & 0.958 \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._tional Neural Networks (CNNs) as backbones, such as resnet18, hourglass-104, resnet101, and res2 net101. The findings are presented in Table 6.  
In their paper [72], [PERSON] and [PERSON] utilized the AU-AIR data as a reference point, employing a range of advanced models to achieve precise and automated detection and classification of road traffic. The YOLOv4 model achieved the highest mean average precision (mAP) score of 25.94% on the AU-AIR dataset. The Faster R-CNN and YOLOv3 models achieved the second and third highest maximum average precision (mAP) scores, with values of 13.77% and 13.33%, respectively.