# SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection
## 4 Experimental Evaluation
### Ablation Experiments  
A series of experiments were conducted to facilitate a comprehensive evaluation of the improved SPANet structure's influence on both recognition precision and speed and to establish the merits of the SPANet structure employed in this research. These experiments involved utilizing the SPANet structure in conjunction with the YOLOv8s algorithm, and their respective outcomes are presented in the initial two rows of Table 2.  
Using the SPANet structure as a path aggregation network increases the model size and GFLOPs, and its FPS decreases, it still manages to improve its mAP0.5 by 2.5% and mAP0.5:0.95 by 1.7% while ensuring real-time performance. The experiments show that using SPANet as a path aggregation network not only improves the accuracy of the original YOLOv8s algorithm and reduces the parameters of the model but also meets the demand of real-time recognition.  
To establish the effectiveness of the employed SPD-Conv module, an experimental setup was devised wherein the SPD-Conv module was integrated with the original YOLOv8s framework, while the unmodified YOLOv8s configuration was used as a basis for comparison. The results of this investigation, as depicted in the third row of Table 2, exhibit the impact of incorporating the SPD-Conv module. As observed in Table 3, incorporating the SPD-Conv module leads to a noteworthy 2.7% improvement in mAP0.5 and a 1.6% enhancement in mAP0.5:0.95. Remarkably, these performance gains are achieved while maintaining a comparable model size, thus attaining an improved balance between accuracy and speed. The conducted experiments convincingly demonstrate that including the SPD-Conv module effectively enhances the detection and recognition capabilities of the models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **mAP0.5/\%** & **mAP0.5:0.95/\%** & **FPS** &
\begin{tabular}{c} **Model** \\ **Size/MB** \\ \end{tabular} & **GFLOPs** \\ \hline YOLOv8s & 43.4 & 19.3 & 94 & 21.48 & 28.5 \\ YOLOv8s + SPANet & 45.9 & 21.0 & 47 & 17.49 & 62.9 \\ YOLOv8s + SPD-Conv & 46.1 & 20.9 & 63 & 24.41 & 45.8 \\ YOLOv8s + SPD + SPANet & 48.3 & 22.7 & 37 & 19.95 & 86.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of different modules on AI-TOD dataset.  
To ascertain the efficacy of the proposed method, the combination of YOLOv8s + SPD-Conv + SPANet is employed, with YOLOv8s + SPD-Conv serving as the comparative approach. The experimental results, presented in the final two rows of Table 2, highlight the impact of incorporating the SPANet structure. Notably, the utilization of the SPANet structure yields a 2.2% improvement in mAP0.5 and a 1.8% enhancement in mAP0.5:0.95. Moreover, this structural modification effectively reduces the number of model parameters, thereby striking a desirable balance between accuracy and speed, despite a slight decrease in FPS. The conducted experiments conclusively demonstrate that concurrently including the SPD-Conv module and adoption of the SPANet structure significantly enhance the model's detection and identification performance while simultaneously minimizing the model's parameter count.  
Figures 5 and 6 show the validation results of YOLOv8s and SP-YOLOv8s on the AI-TOD dataset, respectively, with the GFLOPs of YOLOv8s and SP-YOLOv8s shown in the red boxes.