## 4 Experimental Results and Analysis
### Ablation Experimental  
In order to evaluate the impact of LAWDS, P2-LSCSBD detection head, and loss function WIMIoU on the detection performance of LSOD-YOLOv8n, this paper conducted an ablation experiment, and the specific results are shown in Table 4. The ablation experiment results show that when only the LAWDS module is introduced, the mAP50 is increased  
Figure 8: Comparison of detection performance of algorithms in different scenarios. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **P** & **R** & **mAP50** & **mAP50-95** & **Parameters** &
\begin{tabular}{c} **Detection** \\ **Speed (s)** \\ \end{tabular} \\ \hline YOLOv5n & 0.84 & 0.792 & 0.805 & 0.437 & 2,508,659 & 0.69 \\ YOLOv8n & 0.849 & 0.803 & 0.829 & 0.513 & 3,011,043 & 0.55 \\ YOLOv10n & 0.858 & 0.803 & 0.842 & 0.524 & 2,707,430 & 0.67 \\ LSOD-YOLOv8 & 0.868 & 0.805 & 0.857 & 0.535 & 2,141,477 & 0.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative experimental results of different detection models. by 0.7%, the accuracy is increased by 1.7%, the detection speed is accelerated by 0.058 s, and the number of parameters is reduced by 10%; WIMIoU improves both the accuracy and mAP while keeping the speed and number of parameters unchanged; after adding the P2-LSCSBD detection head alone, the mAP50 is increased by 1.9%, and the number of parameters is slightly reduced; finally, compared with the original YOLOv8n, the LSOD-YOLOv8n algorithm, using all the improved methods, has an increase of 2.8 percentage points in mAP50, an increase of 1.9 percentage points in precision, a reduction of about 28% in model parameters, and a slight increase of 0.09 s in detection time. A series of ablation experiments prove the effectiveness of each module and prove that LSOD-YOLOv8n is the most suitable model in this study. As shown in Figure 9, by comparing the performance curves of different models, LSOD-YOLOv8 demonstrates similar performance in precision, recall, and mean average precision (mAP) to other YOLOv8 models while exhibiting more stable characteristics in certain metrics, particularly after prolonged training. Among all models, LSOD-YOLOv8 reaches a high precision early on and maintains stability throughout subsequent training, indicating its excellent ability to reduce false positives and quickly learn to predict targets accurately. The model's recall is also commendable, ultimately aligning with other models and stabilizing around 0.8, which signifies its effectiveness in detecting positive samples without missing them. In terms of mAP50, LSOD-YOLOv8 performs similarly to other configurations, consistently remaining above 0.8, showcasing its strong target detection capability at lower IoU thresholds. Notably, LSOD-YOLOv8 exhibits slightly superior average precision at stricter IoU thresholds (0.5:0.95) compared to some other configurations. After approximately 600 epochs, the model shows a smooth convergence trend, ultimately stabilizing around 0.6, which reflects its robustness across various complex scenarios. In summary, LSOD-YOLOv8 optimizes the learning strategy, maintaining high precision while achieving good recall and balanced detection performance across multiple IoU thresholds. This model demonstrates high reliability and adaptability in complex tasks, making it particularly suitable for applications that demand precision and stability in target detection. As shown in Figure 10, an analysis of the loss curves for the LSOD-YOLOv8 model reveals significant rapid convergence characteristics during both the training and validation phases, particularly in terms of box loss, DFL loss, and classification loss, all of which maintain low values. This indicates that LSOD-YOLOv8 exhibits superior performance in object localization and classification tasks, demonstrating a strong ability to differentiate between foreground and background objects.