# An Improved Lightweight YOLOv8 Network for Early Small Flame Target Detection
## 6 Conclusions  
This paper aims to present the deployment of a lightweight and highly accurate early small flame target neural network detection model in edge-end embedded devices. The  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Neural Networks** & **mAP0.5/\%** & **FPS/(frame/s)** & **Model Size/MB** \\ \hline YOLOv3-tiny & 70.5 & 91 & 8.2 \\ YOLOv4-tiny & 72.3 & 107 & 10.6 \\ YOLOv5s & 84.3 & 89 & 5.7 \\ YOLOv7-tiny & 85.6 & 83 & 6.8 \\ YOLOv8n & 89.5 & 67 & 6.2 \\ Our & 92.2 & 71 & 4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison experiment of different models.  
Figure 9: Detection performance comparison.  
model has been successfully deployed in various edge-end devices, including RK3588 boards and Nvidia Jetson Nano, and has demonstrated excellent flame target detection capabilities. In terms of algorithm optimization, the original YOLOv8n network is enhanced by modifying the original FPN feature fusion network structure into a BiFPN feature fusion network structure. This involves removing edges that contribute minimally to the feature fusion effect, simplifying the entire feature fusion network and effectively reducing the model size. This approach ensures that the detection accuracy is not significantly compromised. The introduction of the EMA mechanism module allows for the assignment of varying weights to different segments of the source sequence, thereby facilitating the capture of crucial information and enhancing the precision of the model's detection capabilities. In addressing the issue of small flame target detection, this paper introduces the NWD loss function, which is more robust for the detection of smaller objects and limited overlap scenarios. This results in an improvement in the original YOLOv8n network's ability to detect small targets and an enhancement in the model's detection performance.  
The experimental results demonstrate that the size of the enhanced network is 4.8 M, a reduction of 22.5% compared to the original YOLOv8n. This reduction effectively minimizes the model size, facilitating the deployment of the network on embedded devices at the edge. Additionally, the enhanced network exhibits improved detection accuracy for small flame targets, with an mAP0.5 that is 2.7% more accurate than the original YOLOv8n model. Further research will be conducted on the flame target detection algorithm with the objective of further optimization and enhancement. The fusion of smoke and other concomitant features of the fire will be considered with the aim of improving the accuracy of the algorithm's judgment of early small flame targets in more complex and diverse target scenarios.  
Study conception and design: H.D. and Y.L.; data collection: Q.L. and H.Z.; analysis and interpretation of results: H.D. and Z.G.; draft manuscript preparation: H.D. and Y.L. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Hebei Provincial Key RD Program Project: Research on Key Technology of Unattended Active Firefighting Robot in Warehouse Space (22375411D). The data that support the findings of this study are available from the corresponding author, [PERSON], upon reasonable request. We extend our sincere gratitude to all members for their steadfast support of our work. Furthermore, our thanks go to the editors and reviewers whose diligent efforts have greatly contributed to the improvement of this manuscript. The authors declare that they have no conflicts of interest to report regarding the present study.