## 4 Experiments
### Experimental Results  
PDWT-YOLO was compared with other models using the VisDrone-2019 dataset to verify its effectiveness. As Table 1 shows, the values of APM and APL for PDWT-YOLO are lower than those of the Faster R-CNN; however, the number of parameters is also considerably smaller. In addition, Cascade R-CNN has a higher [EMAIL_ADDRESS]:0.95; however, it has a params indicator that is considerably larger than that of PDWT-YOLO. Although the params indicator for PDWT-YOLO is slightly higher, the [EMAIL_ADDRESS]:0.95 and [EMAIL_ADDRESS] increased by 5% and 6.7%, respectively, compared with the baseline YOLOv7-tiny. The PDWT-YOLO outperformed CenterNet, YOLOv3, YOLOv5l, and YOLOv7-tiny in terms of indicators. In general, PDWT-YOLO is superior to these mainstream models in terms of multiscale object detection performance, which also shows that the model can effectively integrate features of different scales and understand features at different scales.  
We listed [EMAIL_ADDRESS] for each category, which can describe in detail what has been improved with our model. As shown in Table 2, the [EMAIL_ADDRESS] of our model is compared with those of other models. Improvements were achieved in each category, particularly in the pedestrian and van categories, which increased by 11% and 8%, respectively. These results show that the detection performance of our method is well improved for most categories of targets.  
Figure 6 shows a comparison of the detection effects of YOLOv7-tiny (Figure 6a) and PDWT-YOLO (Figure 6b) on the VisDrone-2019 test dataset. As shown in the blue box of Figure 6b, we can directly observe that PDWT-YOLO successfully detected more  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **[EMAIL_ADDRESS]:0.95** & **[EMAIL_ADDRESS]** & **Params (M)** & **APS** & **APM** & **APL** \\ \hline Faster R-CNN & 21.9 & 37.1 & 137.1 & 13.1 & 33.6 & 37.1 \\ Cascade R-CNN & **24.5** & 39 & 673 & **15.2** & **36.7** & 39.2 \\ CenterNet & 18.7 & 33.6 & 104.8 & 9.8 & 29.3 & 38.7 \\ YOLOv3 & 16.4 & 31.4 & 59.13 & 8.3 & 26.7 & 36.5 \\ YOLOX & 22.4 & 39.1 & 8.9 & 13.7 & 33.1 & **41.3** \\ YOLOv5l & 20.5 & 36.2 & 46.1 & 12.4 & 29.9 & 36.4 \\ YOLOv7-tiny & 17.5 & 34.5 & **6.2** & 10.4 & 26.5 & 36.5 \\ PDWT-YOLO & 22.5 & **41.2** & 6.44 & 15.1 & 31.8 & 36.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different models using VisDrone-2019 dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method** & **Pedestrian** & **People** & **Bicycle** & **Car** & **Van** & **Truck** & **Tricycle** &
\begin{tabular}{c} **Awning** \\ **Tricycle** \\ \end{tabular} & **Bus** & **Motor** & **[EMAIL_ADDRESS]** \\ \hline YOLOv3 & 12.8 & 7.8 & 4.0 & 43.0 & 23.5 & 16.5 & 9.5 & 5.1 & 29.0 & 12.5 & 31.4 \\ YOLOv5l & 44.4 & 36.8 & **15.6** & 73.9 & 39.2 & **36.2** & 22.6 & 11.9 & 50.5 & 42.8 & 36.2 \\ YOLOv7-tiny & 37.7 & 35.9 & 11.0 & 74.5 & 35.2 & 27.6 & 22.4 & 8.3 & 48.5 & 43.6 & 34.5 \\ PDWT-YOLO & **48.7** & **41.6** & 14.7 & **82.0** & **43.2** & 35.4 & **26.8** & **14.2** & **56.4** & **49.3** & **41.2** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results for individual categories on the VisDrone-2019.  
small objects than those shown in Figure 6a, particularly in the far field of view, which is equivalent to reducing the likelihood of small objects being missed or incorrectly detected. In addition, the detection confidence and the detection accuracy for the object were also improved. For example, the detection confidence ratings for the two white cars in the red boxes in Figure 6b improved, respectively, from 0.73 to 0.78 and 0.92 to 0.95 compared with those in Figure 6a. Hence, it can be concluded that our model improved the ability to detect small objects well.