# FFG-YOLO: Improved YOLOv8 for Target Detection of Lightweight Unmanned Aerial Vehicles
## 4 Results
### Comparison with Other Algorithms  
To evaluate FFG-YOLO's performance, we compared it with those of YOLOv8n, YOLOv10n, YOLOv11n, and real-time detection transformer (RT-DETR). Using an input image resolution of 640 \(\times\) 640, evaluation metrics including mAP@50, mAP@50-95, recall, GFLOPs, and FPS were calculated and are presented in Table 1. FFG-YOLO achieved the best performance with an mAP@50 of 34.7% and mAP@50-95 of 20.1%. While YOLOv10n and YOLOv8n performed slightly better than YOLOv11n, FCOS, and RT-DETR in these metrics, highlighting their robustness, FFG-YOLO's recall of 34.5% was comparable to YOLOv10n and only marginally lower than YOLOv8n and RT-DETR, a negligible difference considering its compact size. Notably, FFG-YOLO excelled in model complexity and inference efficiency. With only 800,000 learnable weights, it is significantly more compact: approximately one-fourth the size of YOLOv8n, one-third of YOLOv11n, and 30-80 times smaller than RT-DETR. In terms of inference speed, FFG-YOLO achieved 162.8 FPS, outperforming YOLOv8n (137.1 FPS), YOLOv11n (100.1 FPS), and RT-DETR (21.1 FPS), underscoring its suitability for resource-constrained deployment. Although FFG-YOLO's GFLOPs (10.2) were slightly higher than YOLOv11n and YOLOv8n, its fewer learnable weights and higher FPS demonstrate an effective balance between computational intensity and bandwidth limitations, ensuring overall efficiency. 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.