# DGBL-YOLOv8s: An Enhanced Object Detection Model for Unmanned Aerial Vehicle Imagery
## 4 Experiment and Result Analysis
### Analysis of Results  
To validate the robustness of the DGBL-YOLOv8s algorithm, a visual analysis of the effective receptive field in the backbone network was performed, comparing scenarios before and after the enhancements. Figure 7 illustrates this comparison, with the left image (a) depicting the baseline network and the right image (b) showcasing the network integrated with the C2f_DWR module:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Baseline** & **P/\%** & **R/\%** & **mAP50/\%** & **Para/M** & **Model Size/MB** \\ \hline Faster-RCNN & 34.6 & 17.0 & 14.4 & 136.7 & 260.8 \\ SSD & 35.0 & 16.7 & 14.2 & 22.8 & 45.6 \\ YOLOv3-Tiny & 26.3 & 17.4 & 14.6 & 64.4 & 122.8 \\ YOLOv4 & 30.5 & 16.0 & 15.7 & 61.9 & 118.2 \\ YOLOv5 & 43.4 & 34.4 & 32.9 & 7.0 & 16.0 \\ YOLOv8s & 47.0 & 35.7 & 36.3 & 11.1 & 21.5 \\ DGBL-YOLOv8s & **53.6** & **42.6** & **44.8** & **7.2** & **14.2** \\ \hline \hline \end{tabular} Note: Bold letters indicate the best experimental results.  
\end{table}
Table 5: Comparative experiments.  
Figure 7: Comparison of the changes in the receptive field of the backbone network.  
To provide a clearer explanation of the changes in the receptive field before and after the improvements, a line chart is used to illustrate the variations in the receptive field of the model's backbone network. (Here, \"Thresh\" represents the threshold, set to 0.2, 0.3, 0.5, and 0.99, respectively; \"Rectangle Side Length\" denotes the side length of the rectangle; and \"Area Ratio\" indicates the corresponding area ratio.) The comparative line chart is shown in Figure 8.  
From the line chart, it is evident that replacing the backbone network with the C2f_DWR module enlarges the model's receptive field, enabling it to capture richer feature information and significantly improving its feature extraction capabilities.  
Subsequently, the VisDrone dataset was selected for visualization under various lighting conditions, different scenarios, and environments with dense targets and complex categories. The left image (a) displays the detection results of YOLOv8s, while the right image (b) presents those of DGBL-YOLOv8s. As illustrated in Figure 9, DGBL-YOLOv8s is capable of identifying more target categories during the daytime compared to the original model, indicating its superior detection capability. From the comparison of images in Figure 10,II, it is evident that DGBL-YOLOv8s can detect more target categories and quantities in the dark, proving its effectiveness in target detection tasks under varying lighting conditions. Furthermore, as shown in the comparisons in Figures 11 and 12, DGBL-YOLOv8s significantly reduces the rates of false positives and missed detections compared to the original model, demonstrating its robustness and accuracy.  
Figure 8: Line graph of receptive field contrast.  
Figure 9: Daytime detection results: (**a**) unimproved detection results and (**b**) improved detection results.  
To demonstrate the efficacy of the enhanced algorithm, this paper compares the mAP (mean average precision) of each category before and after the model improvement, as shown in Figure 13. From the figure, it can be observed that the improved DGBL-YOLOv8s model exhibits varying degrees of enhancement in detection accuracy across all categories compared to the original model.  
Figure 11: Comparison of false detection: (**a**) unimproved detection results and (**b**) improved detection results.  
Figure 10: (**I**) Night detection results: (**a**) unimproved detection results and (**b**) improved detection results. (**II**) Night detection results: (**a**) unimproved detection results and (**b**) improved detection results.