# Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny
## 4 Experiments and Results
### Experiment  
#### 4.4.1 Ablation Experiment  
Table 1 provides a detailed overview of the ablation experiment results for the YOLOv7-tiny algorithm. Each experiment is represented by its corresponding algorithmic improvements, marked with a check (\(\surd\)) or cross (\(\times\)) to indicate the inclusion or exclusion of specific components, such as VarifocalLoss, BiFPN, Partial_C_Detect, AKConv, and DWR. The original YOLOv7-tiny algorithm achieved a performance of [EMAIL_ADDRESS] of 35.3%, [EMAIL_ADDRESS]:0.95 of 18.3%, Precision of 45%, Recall of 38.7%, F1-Score of 41.6%, Params of 22.96 MB, and FLOPs of 13.3 G on the VisDrone2019 dataset. In Experiment A, the VarifocalLoss loss function was added (indicated by check (\(\surd\)) under VarifocalLoss), leading to an improvement in performance, with [EMAIL_ADDRESS] increasing to 36.1%, [EMAIL_ADDRESS]:0.95 rising to 19.5%, and Precision improving to 48%. At the same time, Recall, FLOPs, and Params remained unchanged. The F1-Score also increased from 41.6% to 42.9%. In Experiment B, the feature fusion framework was replaced with BiFPN on top of Experiment B (marked as \(\surd\) under BiFPN), resulting in a slight increase in [EMAIL_ADDRESS] to 36.2%, while [EMAIL_ADDRESS]:0.95 remained at 19.5%. Other indicators showed minor fluctuations, with  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **VarifocalLoss** & **BiFPN** & \ \begin{tabular}{c} **Partial_C\_**-** \\ **Detect** \\ \end{tabular}  & **AKConv** & **DWR** & \begin{tabular}{c} **[EMAIL_ADDRESS]** \\ **(\%)** \\ \end{tabular}  & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **a9.5** & **Precision** \\ \cline{1-1} \cline{6-13} \cline{13-13} \cline{13-13} \cline{13-13} \cline{13-13} \cline{13-13} \  
Precision slightly dropping to 47.3% and Recall increasing to 38.9%. Params and FLOPs changed slightly to 23.72 MB and 13.9 G, respectively. The F1-Score decreased slightly from 42.9% to 42.7%. Although the performance gains from replacing the feature fusion framework with BiFPN in Experiment B were modest, the multi-scale feature fusion introduced by BiFPN provided a more comprehensive feature input for subsequent modules (such as Partial_C_Detect and AKConv), ensuring higher-quality feature extraction in subsequent modules, which is crucial for enhancing algorithm performance. In Experiment C, by incorporating the Partial_C_Detect detection head into Experiment C (indicated by check (\(\surd\)) under Partial_C_Detect), the performance improved significantly, with [EMAIL_ADDRESS] reaching 37.7%, [EMAIL_ADDRESS]:0.95 reaching 20.4%, Precision increasing to 48.2%, Recall rising to 39.1%, F1-Score increasing to 43.2%, Params slightly increasing to 25.76 MB, and FLOPs at 14.9 G.  
In Experiment D, the standard convolution (Conv) was replaced with Adaptive Kernel Convolution (AKConv) on top of Experiment D (marked as \(\surd\) under AKConv), further improving [EMAIL_ADDRESS] to 37.9%, [EMAIL_ADDRESS] to 20.8%, Precision to 48.4%, and Recall to 39.3%. The F1-Score improved slightly to 43.4%. Params decreased to 23.28 MB, and FLOPs decreased to 13.2 G.  
Finally, in Experiment E, the addition of the Dilated Wide Residual (DWR) attention module (indicated by \(\surd\) under DWR) increased the [EMAIL_ADDRESS] to 38.2%, [EMAIL_ADDRESS]:0.95 to 21.2%, Precision to 49.5%, Recall to 39.5%, F1-Score increased to 43.9%, while Params increased to 28.98 MB, and FLOPs increased to 16.2 G.  
To demonstrate the superiority of the improved model, Figure 9 compares the [EMAIL_ADDRESS], Precision, and Recall trends during training between YOLOv7-tiny and the improved model (Ours). The results show that the improved model converges faster and exhibits significant advantages across all performance metrics throughout the training process. Figure 9: Comparison of [EMAIL_ADDRESS], Precision, and Recall between YOLOv7-tiny and Ours during training.