# FFG-YOLO: Improved YOLOv8 for Target Detection of Lightweight Unmanned Aerial Vehicles
## 4 Results
### Comparison with Other Algorithms
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Backbone** & \begin{tabular}{c} **mAP@50** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **mAP@50–95** \\ **(\%)** \\ \end{tabular} & **Recall (\%)** & **GFLOPs** &
\begin{tabular}{c} **Learnable** \\ **Weights** \\ **(Million)** \\ \end{tabular} & **FPS** \\ \hline YOLOv10n & CSPNet & 34.6 & 20.2 & 34.6 & 8.4 & 2.7 & 85.3 \\ YOLOv8n & CSPDarkNet53 & 34.6 & 19.7 & 35.1 & 8.1 & 3.0 & 137.1 \\ YOLOv11n & CSPDarknet & 34.2 & 20 & 33.5 & 6.3 & 2.6 & 100.1 \\ RT-DETR & ResNet-50 & 35.1 & 19.8 & 37.4 & 100.6 & 28.5 & 21.1 \\ FFG-YOLO & CSPDarknet & 34.7 & 20.1 & 34.5 & 10.2 & 0.8 & 162.8 \\ \hline \hline \end{tabular}  
* To further assess FFG-YOLO’s detection capability across target categories, we compared mAP@50 of the algorithms for ten dataset categories, including people (pedestrians and other people), non-vehicles (bicycle, tricycle, awning-tricycle, and motorcycle), and vehicles (car, van, truck, and bus) (Table 2).  
\end{table} 
Table 1: Model comparison for image resolution of 640 \(\times\) 640. \begin{table} \begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Pedestrian** & \
\begin{tabular}{c} **Other** \\ **People** \\ \end{tabular} & **Bicycle** & **Car** & **Van** & **Truck** & **Ticycle** &
\begin{tabular}{c} **Awning-** \\ **Ticycle** \\ \end{tabular} & **Bus** & **Motor** \\ \hline YOLOv10 & 35.5 & 29 & 11 & 76.4 & 37.8 & 31.5 & 22.6 & 12.8 & 50.2 & 39 \\ YOLOv8 & 36.7 & 29.5 & 9.59 & 76.6 & 37.81 & 31.7 & 24.6 & 12.6 & 48 & 38.9 \\ YOLOv11 & 35.6 & 28.7 & 9.04 & 75.8 & 37.9 & 31 & 22.5 & 12.4 & 51.4 & 37.4 \\ RT-DETR & 35.7 & 25.7 & 14 & 74 & 39.5 & 38.2 & 27.2 & 12.8 & 47.4 & 36.8 \\ FFG-YOLO & 39.3 & 31.3 & 9.9 & 78.5 & 40.5 & 24.1 & 22 & 12.6 & 46.3 & 41.2 \\ \hline \hline \end{tabular}   
* FFG-YOLO demonstrated superior detection performance in most categories, particularly for dense crowds and variant patterns. Specifically, its mAP reached 39.3% for pedestrians and 31.3% for other people, significantly outperforming YOLOv8 (36.7 and 29.5%) and YOLOv11 (35.6 and 28.7%). This highlighted the strong feature perception and segmentation capabilities of FFG-YOLO in the dense distribution of objects and significant occlusion. The advantage stemmed from the model’s FCB and FEB, which enhanced fine-grained feature extraction and semantic fusion and improved pedestrian target recognition. FFG-YOLO demonstrated exceptional performance in vehicle detection, with an mAP@50 of 78.5% for cars and 40.5% for vans, the highest among all models. For cars, FFG-YOLO showed a 2% higher mAP@50 than YOLOv8, highlighting the robust detection of large vehicles. While the detection accuracies for trucks and buses were slightly lower than those of RT-DETR, such a difference is acceptable given the model’s lightweight design. For non-motorized vehicles, FFG-YOLO showed an mAP@50 of 9.9% for bicycles, 22.0% for tricycles, and 41.2% for motorcycles. Notably, for vehicles, FFG-YOLO's mAP@50 was 41.2%, outperforming YOLOv8 (38.9%) and RT-DETR (36.8%). This evidenced an accurate detection of fast-moving small targets. Although bicycle detection ability was worse than that of RT-DETR (14.0%), FFG-YOLO surpassed YOLO models in detecting awning-tricycles. * FFG-YOLO had a lightweight design and high-speed inference while preserving detection accuracy due to its network architecture. The FCB module enhanced multi-scale feature extraction through channel weighting and bidirectional feature fusion, significantly improving small-target detection accuracy. The FEB module strengthened semantic modeling by integrating contextual information through dilated convolution, while the GCAB module enhanced foreground target perception to suppress redundant background interference. Additionally, the WDLoss function improved the accuracy in bounding box localization and regression stability, further enhancing detection performance.