## 3 Materials and Methods
### YOLOv8 Model  
YOLOv8 [40] is a highly efficient deep learning model in the field of target detection, which inherits the tradition of YOLO series algorithms and is capable of achieving high accuracy and high-speed target detection. YOLOv8 is based on the CSP (Cross-Stage Partial) structure of YOLOv5 [41] in the backbone network, but with optimization of the module design. YOLOv8 has replaced the C3 module in YOLOv5 with the C2f module to further reduce the model size and improve computational efficiency. YOLOv8 replaces the C3 module in YOLOv5 with the C2f module to further reduce the model size and improve computational efficiency. Meanwhile, it adopts the improved Path Aggregation Network (PANet) as the feature fusion module and introduces the CIoU loss function, which effectively improves the accuracy of target frame regression. In addition, YOLOv8 has also optimized the data enhancement and training strategy, which makes the model's performance more stable under different scales and backgrounds. The model structure of YOLOv8 is shown in Figure 2.  
The network structure of YOLOv8 can be divided into several important parts: the backbone, the Feature Pyramid, and finally the head. First, YOLOv8's backbone network uses an efficient structure (CSPDarkNet) to extract features from images. This network architecture is optimized to extract richer image features while ensuring lower computational complexity, providing more accurate information for subsequent detection tasks. Next is the Feature Pyramid, or neck part, where YOLOv8 adopts a more advanced feature fusion approach. In the target detection task, targets of different scales can produce features of  
Figure 2: The model structure of YOLOv8.  
different sizes in the image, and the Feature Pyramid Network (FPN) can effectively deal with this problem. The combination of FPN and PANNet (Path Aggregation Network) is used in YOLOv8 to further enhance the fusion of multi-scale features. With this design, YOLOv8 is able to better handle both small and large targets and maintain good detection accuracy in both multi-scale images. Finally, there is the head network of YOLOv8, which is responsible for the final target classification and bounding box regression. YOLOv8 uses the Adaptive Feature Layer and the new detection head for both classification and localization tasks, making the network not only able to handle more complex scenarios effectively but also ensuring efficient computational performance.  
However, although YOLOv8 performs well in most application scenarios, there are still some performance bottlenecks in some specific cases. For example, in terms of small target detection and robustness in complex backgrounds, YOLOv8 may suffer from leakage and false detection problems. This is mainly due to the limited ability of its feature extraction network to capture fine-grained small targets, and it may be difficult to effectively differentiate between targets and interferences in complex backgrounds. Therefore, this study proposes further improvements based on YOLOv8, aiming to enhance the performance of small target detection, improve the robustness of the model in complex backgrounds, and optimize the computational efficiency to adapt to a wider range of practical applications.