## 4 Experimental Results and Analysis
### Comparative Experiment of Different Detection Models  
In order to more intuitively demonstrate the advantages of the improved algorithm in cigarette detection, we compared the existing mainstream algorithms. By comparing the precision value and recall curve of each model in the figure, it can be concluded that the LSOD-YOLOv8n algorithm is better than the YOLOv8n benchmark model and the advanced YOLOv5n and YOLOv10n models in detection performance and also has a significant advantage in detection accuracy. \begin{table}
\begin{tabular}{c c} \hline
**Platform** & **Configuration Information** \\ \hline System & Windows 10 \\ GPUs & NVIDIA GeForce RTX 4070 \\ CPU & 13 th Gen intel(R) Core (TM) i5-13490F 2.50 GHz \\ Language & Python 3.9 \\ GPU calculate platform & CUDA 12.1 \\ Deep learning framework & Pytorch 2.2.0 \\ \hline \end{tabular}
\end{table} 
Table 1: Computer configuration. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **P** & **R** & **mAP50** & **mAP50-95** & **Parameters** & **Layers** & **Detection Speed (s)** & **GFLOPs** \\ \hline yolov8n & 0.849 & 0.803 & 0.829 & 0.513 & 3,011,043 & 225 & 0.55 & 8.2 \\ yolov8-LAWDS (all) & 0.866 & 0.784 & 0.837 & 0.516 & 2,681,123 & 260 & 0.576 & 8.1 \\ yolov8-LAWDS (neck) & 0.871 & 0.782 & 0.831 & 0.516 & 2,959,331 & 239 & 0.54 & 8.3 \\ yolov8-LAWDS (backbone) & 0.866 & 0.796 & 0.836 & 0.506 & 2,732,835 & 246 & 0.492 & 8 \\ \hline \end{tabular}
\end{table} 
Table 2: Data table of experimental metrics of LAWDS module at different positions. As shown in Figure 7, the experimental results show that LSOD-YOLOv8 shows significant comprehensive performance advantages in target detection tasks. The model achieves dynamic balance optimization of precision-recall by introducing LAWDS and P2-LSCSD detection head modules: after 600 iterations, the accuracy, recall, and mAP values of each model tend to be stable, and the final accuracy is above 0.8, the recall is around 0.8, the mAP50 is above 0.8, and the mAP50-95 remains around 0.5, but LSOD-YOLOv8 presents the best detection performance. In summary, LSOD-YOLOv8 achieves the best trade-off between accuracy, robustness, and efficiency in detection tasks through innovative architecture improvements and loss function optimization, providing an important reference for the technical development of lightweight detection models. From the experimental results in Table 3, it can be seen that compared with YOLOv5n, YOLOv8n, and YOLOv10n, LSOD-YOLOv8n is more suitable for cigarette detection. In terms of key indicators, such as mAP50, mAP50-95, accuracy, and recall, LSOD-Yolov8n has shown obvious advantages, and the number of parameters has been greatly reduced. It is only slightly slower than the YOLOv5n model in detection speed by 0.09s. Considering the application scenario of our cigarette detection algorithm for small targets in public spaces, where public safety requirements are paramount, we prioritize detection accuracy as the primary optimization objective throughout our algorithm development while maintaining reasonable inference speed. As demonstrated in Table 3, we adopt mAP50 as the principal evaluation metric to ensure high detection precision. Concurrently, we select network architectures with minimized parameters to achieve satisfactory inference efficiency, thereby balancing accuracy and computational performance for practical deployment in surveillance environments. Our model achieved a 2.8% improvement in the mAP value but was only 0.09 \(s\) slower in detection speed, which is acceptable for our application scenario. The above comparative experimental results clearly show that the LSOD-YOLOv8n algorithm proposed in this paper exhibits superior detection performance in the field of cigarette detection. Figure 7: Mainstream algorithm performance comparison.