# FFG-YOLO: Improved YOLOv8 for Target Detection of Lightweight Unmanned Aerial Vehicles
## 5 Discussion  
FFG-YOLO was developed in this study as a lightweight and small-target detection method based on YOLOv8n. It significantly reduces model size, being approximately one-fourth of YOLOv8n, one-third of YOLOv11n, and more compact than other models such as RT-DETR. It had 800,000 learnable weights, a 67% reduction from the baseline YOLOv8n. This reduced model size is crucial for deployment on embedded platforms with limited memory and storage. FFG-YOLO achieved 162.8 FPS, outperforming YOLOv8n (137.1 FPS) and YOLOv11n (100.1 FPS) on the experimental setup (RTX 4090). Its superior efficiency on high-end GPUs suggests that it maintains a relatively high performance compared to other models when scaled down to embedded systems. FFG-YOLO addresses the stringent requirements of UAVs in terms of real-time performance and deployment, which are inherently resource-constrained. This indicates that FFG-YOLO prioritizes efficiency to meet the demands of edge computing chips often found in UAVs. The integration of the FEB, FCB, and GCAB modules enhances feature extraction and fusion while maintaining a lightweight architecture. The FEB module enhances small-target recognition accuracy by 2.1% through multi-branch convolution to expand the receptive field, while increasing the model size by 8% and reducing inference speed by 5%. The FCB module improves mAP@50 by 1.7% via channel-adaptive weighted cross-scale fusion, with parameter growth of less than 3% and negligible impact on speed (<2%). The GCAB module boosts and increases recall in complex scenes by 3.2% through global context modeling, adding 5% more parameters (about 40,000) and increasing inference latency by 4%. WDLoss reduces small-target localization errors by 12% and improves mAP@50 by 0.9% compared with CIoU/DIoU, as it measures box distance via optimal transport theory, without increasing model size or inference time--unlike some complex IoU variants that may introduce minor computational overhead. The WDLoss function improves the accuracy and stability of bounding box localization, which is vital for precise detection on platforms with limited computational power. These architectural and optimization choices contribute to its ability to perform effectively without requiring extensive resources. FFG-YOLO significantly reduced model size, high inference speed, and targeted design for lightweight deployment make it well-suited for embedded platforms, such as Jetson Nano and TX2, where computational resources and power consumption are critical constraints. FFG-YOLO enhances accuracy, model size, and speed through the synergistic contributions of its integrated modules and the WDLoss function. The WDLoss function optimizes the target frame regression. It is used to accurately measure the difference between predicted and real bounding boxes, especially for small targets with blurred boundaries or confusing backgrounds, thereby enhancing pixel-level positioning accuracy. In the ablation experiment, when WDLoss was added to the YOLOv8n baseline, it resulted in a slight increase in mAP@50 (from 34.6 to 34.62%) and mAP@50-95 (from 19.7 to 20.0%). This indicates its stability and discriminative properties in regression head optimization and a positive effect on bounding box aggregation learning. The FFG modules collectively aim to strengthen feature extraction, minimize semantic bias, and enhance context awareness for improved small-target detection. The FEB module enhances multi-scale feature extraction for small targets through multi-branch convolution and residual connections, expanding sensory information without increasing the number of parameters. This helps to capture contextual information and improve semantic richness, while the FCB detection addresses scale variations and dispersed feature map semantics. It uses a channel-adaptive weighting strategy and a bidirectional feature flow mechanism to enable efficient multi-scale feature fusion, minimizing semantic bias and enhancing fine-grain and semantic richness.