# FFG-YOLO: Improved YOLOv8 for Target Detection of Lightweight Unmanned Aerial Vehicles
## 4 Results
### Evaluation Metrics  
In this study, precision, recall, mean average precision (MAP), and frames per second (FPS) were chosen as evaluation metrics because they are standard, comprehensive, and relevant for assessing small object detection models, especially in real-time applications, such as UAV aerial photography. The metrics were selected according to the specific challenges for lightweight small-target detection in UAV aerial imagery, ensuring alignment with algorithmic performance and practical deployment requirements. Precision (P) and recall (R) are metrics to evaluate the model's performance in terms of correctness and completeness of detection. Precision measures the proportion of correctly identified true positives out of all positive predictions made by the model. It is crucial to minimize false detection, which is critical in object detection, where erroneous detections lead to incorrect information. This is essential for reducing false alarms in cluttered backgrounds (e.g., distinguishing small vehicles from similar-looking road textures). \[P=\frac{TP}{TP+FP} \tag{13}\]  
where \(TP\) is the number of true positives and \(FP\) is the number of false positives. Recall measures the proportion of correctly identified positive predictions (true positives) out of all actual positive instances. It is vital to ensure that the model does not miss important targets, which is especially important for surveillance using images. \[R=\frac{TP}{TP+FN} \tag{14}\]  
where \(TP\) is the number of true positives, \(TP\) is the number of true positives, and \(FN\) is the number of false negatives. mAP is the most widely accepted and robust metric for evaluating overall performance in object detection. It provides a single score that summarizes both precision and recall across various confidence thresholds and object classes. mAP@50 is a commonly used metric to evaluate detection accuracy at a relatively lenient IoU threshold of 0.5. It indicates whether objects are localized correctly. mAP@50-95 is a more stringent metric, calculating the average mAP across multiple IoU thresholds (from 0.5 to 0.95 with a step of 0.05). This is important for small-target detection with a high pixel-level positioning accuracy. A high mAP@50-95 indicates that the model precisely localizes objects, which is critical for crowded scenes or when fine-grained object boundaries are important. \[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{15}\]  
\[AP=\int\limits_{0}^{1}P(R)dR \tag{16}\]  
where \(N\) is the number of classes, \(APi\) is the average precision for each class \(I\), \(P\) is precision, and \(R\) is recall. FPS is a direct measure of the model's inference speed, indicating how many images the model can process per second. FPS is a key metric to measure the inference speed of a target detection algorithm, representing the number of images a model can process and analyze in one second. For UAVs operating with limited computational resources and requiring immediate responses, high FPS ensures that the detection system can keep pace with video streams and provide timely information for navigation, control, or surveillance. In this study, FPS is used to validate the real-time performance of FFG-YOLO, confirming that its lightweight design meets the speed requirements of UAV deployment. \[FPS=\frac{N_{frame}}{T} \tag{17}\]  
where \(N_{frame}\) is the number of frames, and \(T\) is the total time in seconds. Collectively, these metrics provide a balanced evaluation, assessing the accuracy of detection (\(P\), \(R\), and mAP) and the computational efficiency (FPS) of the model, which are the primary concerns for deploying object detection systems on resource-constrained platforms, such as UAVs. Learnable weights (model size) indicate the total number of parameters in the model, measured in millions (M). It is a direct measure of model size and complexity. A lower number of learnable weights signifies a lightweight model, which is crucial for deployment on resource-constrained platforms, such as UAVs.