# FFG-YOLO: Improved YOLOv8 for Target Detection of Lightweight Unmanned Aerial Vehicles
## 2 YOLOv8  
As a representative algorithm in target detection, YOLOv8 demonstrates advantages in UAV aerial photography due to its network structure (Figure 1). In terms of feature representation efficiency, YOLOv8 surpasses traditional convolutional networks as it fuses cross-stage features in the C2F module. Compared with the C3 module first used in YOLOv5, the C2F module employs dense gradient paths to efficiently fuse shallow high-resolution features with deep semantic ones across layers, forming a \"detail-semantic\" dual-enhanced feature pyramid. The C2F module effectively detects small targets, which is important for detecting objects in UAV photography. To identify the contours of small targets, shallow detail features must be identified due to their low pixel ratio to distinguish semantic features from cluttered backgrounds. The C2F module minimizes feature loss during transmission through gradient flow optimization, significantly enhancing the discriminability of small targets. While newer YOLO versions, such as YOLOv9, YOLOv10, and YOLOv11, offer enhanced object detection capabilities, YOLOv8 maintains distinct advantages as a preferred choice for specific applications. Its longer adoption period has fostered a mature and stable ecosystem, providing extensive documentation, a robust community [23], and thoroughly tested implementations [23; 24]. This widespread usage ensures a reliable performance baseline competitive across a wide range of common object detection tasks. In many practical applications, the incremental performance gains of newer versions, which might achieve state-of-the-art results on specific benchmarks or in NMS-free inference, may not signifi  
Figure 1: Structure of the FFG-YOLO model. cantly outweigh YOLOv8's proven stability and ease of use [3]. Furthermore, YOLOv8 and its variants offer superior computational efficiency for lighter models deployed on edge devices due to their simpler architecture compared to newer iterations [23]. In this study, we focused on addressing the challenges of small-target detection in UAV imagery through novel architectural innovations rather than incremental improvements to existing YOLOv8 variants or directly enhancing newer YOLO versions. To develop lightweight solutions for resource-constrained UAV platforms, modules were designed, and the loss function was optimized. WDLoss was used for the enhancements of small-target detection along with FEB, FCB, and GCAB modules. YOLOv8n allows for an effective isolation and demonstration of their effectiveness, unconfounded by the architectural changes (e.g., new backbone blocks or head designs) introduced in newer YOLO variants. Newer YOLO versions often increase model complexity to enhance overall accuracy, directly conflicting with lightweight deployment. While YOLOv10n and YOLOv11n show marginal improvements in general datasets, their parameter counts and computational overhead remain higher than YOLOv8n, making them less suitable as a foundation for our lightweight design. While UAV-YOLOv8, LAYN-YOLOv8, and efficient YOLOv8 target similar application domains, they do not incorporate feature fusion, loss function, or anchor-free mechanisms. In scenarios where an existing system is already well-optimized for YOLOv8, or if the performance improvements from migrating to a newer version are marginal, YOLOv8 can be a cost-effective selection. This avoids unnecessary development time and optimization expenses without sacrificing significant real-world performance [24]. While newer YOLO versions have enhanced their performance, YOLOv8 remains a strong, reliable, and versatile solution due to its maturity, established performance, and simpler deployment. In small-target detection, YOLOv8 uses an anchor-free, decoupled detection head. Unlike traditional anchor-based methods that rely on preset bounding boxes, YOLOv8 detects UAV-captured objects in diverse scale ranges, which often leads to missing small targets.