## 5 Discussion  
Cigarette detection represents a critical component of public safety management systems. Despite the advancements in object detection algorithms, challenges persist due to the inherent limitations of cigarette-like targets, which exhibit small size and distinctive feature sets. To address these challenges, we propose an optimized lightweight target detection framework based on the YOLOv8 algorithm, aiming to enhance detection accuracy while maintaining computational efficiency. Our experimental results demonstrate that the proposed P2-LSCSBD-YOLOv8 model achieves superior performance across multiple metrics. Compared to the baseline model, our improved architecture reduces the total parameter count by approximately 28%, while maintaining comparable accuracy (mAP50 improves by 2.8% and mAP50-95 increases by 2.2%). The enhanced detection head incorporates multi-scale feature fusion capabilities, which are particularly beneficial for real-world applications where objects may appear at varying scales.  
Despite these improvements, several limitations remain. First, the lightweight adaptive weight downsampling module, while compensating for information loss inherent in traditional convolutions, could be further optimized to preserve critical features without significantly increasing computational overhead. Second, although the P2-LSCSBD detection head achieves effective multi-scale integration, detection speed remains a potential bottleneck under high-resolution imaging requirements. Looking forward, we suggest several promising directions for future research. First, integrating more efficient attention mechanisms, such as deformable attention modules or scaled dot-product attention variants, could further enhance computational efficiency while maintaining detection accuracy. Second, extending the framework to handle multi-task learning scenarios, such as simultaneous target classification and bounding-box regression, may enable more resource-efficient  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Dataset** & **P** & **R** & **mAP50** & **mAP50-95** \\ \hline YOLOv8n & cigarette & 0.849 & 0.803 & 0.829 & 0.513 \\ LSOD-YOLOv8 & cigarette & 0.868 & 0.805 & 0.857 & 0.535 \\ YOLOv8n & fire & 0.543 & 0.607 & 0.519 & 0.251 \\ LSOD-YOLOv8 & fire & 0.706 & 0.543 & 0.603 & 0.31 \\ YOLOv8n & waste & 0.985 & 0.987 & 0.994 & 0.873 \\ LSOD-YOLOv8 & waste & 0.996 & 0.991 & 0.995 & 0.911 \\ YOLOv8n & coco & 0.734 & 0.687 & 0.71 & 0.539 \\ LSOD-YOLOv8 & coco & 0.759 & 0.694 & 0.753 & 0.578 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model performance across different datasets.  
solutions with improved performance across multiple detection tasks. Moreover, exploring interdisciplinary applications within public safety management could expand the utility of our proposed architecture beyond traditional surveillance contexts. For instance, integrating cigarette detection with smoke detection systems may provide enhanced capabilities for risk assessment in challenging environments where conventional surveillance methods are ineffective.  
In conclusion, this study presents significant advancements in lightweight target detection frameworks, particularly for small object recognition tasks. By addressing existing limitations and exploring novel research directions, we hope to contribute meaningful improvements to the field of public safety management and related domains.