# FFG-YOLO: Improved YOLOv8 for Target Detection of Lightweight Unmanned Aerial Vehicles
## 1 Introduction  
Target detection is crucial for intelligent transportation and automated driving, with deep learning algorithms significantly improving its accuracy and precision. These algorithms are categorized into single-stage and two-stage algorithms. Single-stage algorithms, such as the You Only Look Once (YOLO) series [1, 2, 3, 4, 5, 6, 7, 8], single-shot multiBox detector (SSD) [9], and RetinaNet [10], rapidly output target locations and categories, making them well-suited for fast processing, such as unmanned aerial vehicles (UAVs) aerial photography. Two-stage algorithms, including Faster Regions with Convolutional Neural Networks (R-CNN) [11], Mask R-CNN [12], and Cascade R-CNN [13], show higher detection accuracies by effectively identifying potential target regions but require higher computational resources and lower efficiency, limiting their widespread application. While single-stage methods show similar speeds and accuracy to two-stage algorithms, they often struggle with accurately detecting small targets. UAVs offer significant advantages in taking aerial photographs due to their portability, ease of operation, and adaptability to diverse terrains [14]. Equipped with intelligent visualsystems, UAVs perform target detection, identification, localization, and tracking through wide-angle and high-density aerial imagery that provides rich semantic details. However, UAV-based target detection has challenges in detecting small targets. Such small targets are often occluded or taken in images at low pixel ratios, leading to increased noise, requiring enhanced spatial sensitivity. Moreover, diverse image resolutions of small targets cause large variations in target scales and semantic information loss. UAVs' hardware constraints in power supply and flight time also limit small-target detection accuracy, necessitating a balance between lightweight design and high-resolution processing [15]. Therefore, various strategies have been proposed to improve small-target detection in UAV-captured images. [PERSON] et al. [16] enhanced feature fusion and scale perception using SSD for small targets, yet reliable detection in densely occluded images remained an issue. [PERSON] et al. [17] introduced the SPD-Conv module into YOLOv5 for pedestrian detection, preserving fine-grained features during downsampling; however, it struggled to capture global semantic information for larger targets in complex backgrounds. [PERSON]. [18] utilized YOLOv8 with a C2f module, FPN, and LSCD to balance computational load and accuracy. Building on this, [PERSON] et al. [19] developed OD-YOLO from YOLOv8, incorporating deformable convolution for small-sized, fuzzy, and geometrically deformed targets in remote sensing images. Despite these advances, complex backgrounds introduce significant noise that hinders robust feature extraction, requiring further improvement in detection adaptability. [PERSON] et al. [20,21] optimized YOLOv8 with GhostNet for computational efficiency and soft non-maximum suppression (S-NMS) for dense small targets, but this method was prone to miss or false detections due to insufficient contextual modeling in complex or crowded scenes. More recently, YOLOv11 models, incorporating modules such as Cross-Stage Partial with Kernel Size 2 (C3k2), Spatial Pyramid Pooling-Fast (SPPF), and Cross-Stage Partial with Pyramid Squeeze Attention (C2 PSA), are used for enhanced parameter accuracy balance and higher efficiency. [PERSON] et al. [22] improved YOLOv11's SPPF layer for small-target capture in UAV photos, but the resulting network complexity limited UAV deployment. Despite such advancements, current target detection methods still have limitations in extracting multi-dimensional image features and detailed semantic information. Algorithms relying on complex multi-feature fusion often lead to an exponential increase in parameters and reduced inference efficiency.