# Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny
## 4 Experiments and Results
### Experiment
#### 4.4.2 Comparative Experiment  
This experiment validates the performance of different algorithms in object-detection tasks by comparing the detection accuracy, parameter count, and floating-point operations (FLOPs) of several lightweight algorithms on the VisDrone2019 dataset. Due to the high resolution of drone images in the VisDrone2019 dataset, a large number of small objects, and the presence of many similar-looking objects, the features of the objects are easily weakened, leading to frequent missed and false detections. Consequently, the detection accuracy of lightweight algorithms is generally low. The comparison results are shown in Table 2.  
Although Yolov3-tiny and Yolov4-tiny are more efficient in terms of parameter count and FLOPs, with 33.51 MB, 13.0 G and 22.41 MB, 14.2 G, respectively, their detection accuracy is relatively low, with [EMAIL_ADDRESS] of only 19.9% and 25.7%. This indicates that they are not suitable for complex UAV image analysis tasks. In contrast, Yolov5s and Yolov7-tiny show significant improvements in [EMAIL_ADDRESS], reaching 33.3% and 35.3%, respectively, with Yolov7-tiny having a parameter count of 22.96 MB and FLOPs of 13.9 G.  
Yolov6m's detection accuracy is [EMAIL_ADDRESS] of 31.7%, but it has a relatively large parameter count and FLOPs, at 34.24 MB and 82.0 G, respectively. YoloX, on the other hand, performs well in terms of parameter count and FLOPs, with 9.0 MB and 26.8 G, but its [EMAIL_ADDRESS] is only 25.5%, indicating insufficient accuracy. PPYoloE achieves a [EMAIL_ADDRESS] of 32.4%, with a parameter count of only 8.9 MB and FLOPs of 31.8 G. While Faster R-CNN shows moderate detection accuracy with a [EMAIL_ADDRESS] of 22.8%, it has the highest resource consumption among all algorithms, with a parameter count of 136.9 MB and FLOPs of 180 G. Based on the transformer architecture, the Swin \begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **[EMAIL_ADDRESS]** & **Params (MB)** & **FLOPs (G)** \\ \hline Yolov3-tiny & 19.9\% & 33.51 & 13.0 \\ Yolov4-tiny & 25.7\% & 22.41 & 14.2 \\ Yolov5s & 33.3\% & 7.0 & 15.9 \\ Yolov7-tiny & 35.3\% & 22.96 & 13.9 \\ Yolov6m & 31.70\% & 34.24 & 82.0 \\ YolovX & 25.5\% & 9.0 & 26.8 \\ PPYoloE & 32.4\% & 8.9 & 31.8 \\ Faster R-CNN & 22.8\% & 136.9 & 180 \\ Swin-transformer & 31.6\% & 50.0 & 58.0 \\ Yolov8s & 37.9\% & 10.59 & 28.7 \\ Yolov8n & 31.4\% & 2.86 & 8.2 \\ Yolov8m & 35.9\% & 24.65 & 78.7 \\ Yolov9-c & 37.0\% & 24.13 & 102.1 \\ Yolov9s & 37.6\% & 9.6 & 26.7 \\ Yolov10n & 29.0\% & 2.3 & 8.2 \\ BGF-Yolov10 & 32.0\% & 2.0 & 8.6 \\ Ours & 38.2\% & 28.98 & 16.2 \\ \hline \end{tabular}
\end{table} 
Table 2: Detection accuracy, Params, and FLOPs of different algorithms. transformer model reaches a [EMAIL_ADDRESS] of 31.6%, with a parameter count of 50.0 MB and FLOPs of 58.0 G.  
The YOLOv8 series exhibits varied performance across its different versions. YOLOv8s achieves the highest detection accuracy in the series, with a [EMAIL_ADDRESS] of 37.9%, while maintaining moderate resource consumption with 10.59 M parameters and 28.7 G FLOPs. In contrast, YOLOv8n is a lightweight version with only 2.86 M parameters and 8.2 G FLOPs, but its detection accuracy decreases to a [EMAIL_ADDRESS] of 31.4%. YOLOv8m balances accuracy and resource consumption, achieving a [EMAIL_ADDRESS] of 35.9%, with 24.65 M parameters and 78.7 G FLOPs, making it suitable for scenarios requiring higher accuracy with sufficient resources. YOLOv9-c improves detection accuracy, reaching a [EMAIL_ADDRESS] of 37.0%, but with 24.13 M parameters and a high computational demand of 102.1 G FLOPs. In contrast, YOLOv9s achieves a better balance between resource consumption and detection performance, with 9.6 M parameters and 26.7 G FLOPs, achieving a [EMAIL_ADDRESS] of 37.6%, making it an efficient choice for high-accuracy tasks under limited resources. Among the newer models, YOLOv10n achieves a [EMAIL_ADDRESS] of 29.0% with only 2.3 M parameters and 8.2 G FLOPs, showcasing its efficiency for lightweight applications.