# An Improved Lightweight YOLOv8 Network for Early Small Flame Target Detection
## 5 Analysis of Results
### Ablation Experiment  
In order to more intuitively verify the effectiveness of the improvements to the original YOLOv8 model under the same hardware environment and using the same dataset, the experimental results are as follows. In the table, \"\(\surd\)\" indicates that the improvement module was added to the original model; otherwise, the improvement module was not used. As illustrated in Table 3, the replacement of the FPN feature fusion module with the BiFPN module resulted in a reduction of 2 MB in the model size, an improvement of 0.9% in accuracy, and an increase of 0.1 s in the single detection time. The results demonstrate that the BiFPN module is more suitable for lightweight neural network design, with a reduction in impact on detection accuracy through the removal of redundant networks in the fused feature network computation. Furthermore, the passing of features in both directions maintains high recognition accuracy, making it more suitable for deployment at the edge end. Improvement 2 incorporates the EMA mechanism into the original YOLOv8 network. Prior attention mechanism models facilitate cross-channel relationships through channel dimensionality reduction, but this approach has the drawback of increased computation. The EMA, in contrast, employs dimensionality reduction in the channel to group multiple sub-features, ensuring the uniform distribution of spatial semantic features across each feature group. This results in a model weight that is essentially equivalent to that of the original network while significantly enhancing detection accuracy. The network is  
Figure 8: Average precision comparison. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Feature Fusion**} & \multirow{2}{*}{**Attention Mechanism**} & \multirow{2}{*}{**Loss Function**} & \multirow{2}{*}{**mAP0.5/\%**} & \multicolumn{2}{c}{**Single-Frame Detection**} & \multirow{2}{*}{**Model Size/MB**} \\  & & & & & & **Time/ms** & \\ \hline Yolov8n & & & & 89.5 & 14.8 & 6.2 \\ Improvement 1 & \(\surd\) & & & 90.4 & 15.0 & 4.2 \\ Improvement 2 & & \(\surd\) & & 90.2 & 13.2 & 6.3 \\ Improvement 3 & & & \(\surd\) & 91.2 & 15.7 & 7.2 \\ Improvement 4 & \(\surd\) & \(\surd\) & & 90.8 & 13.5 & 4.5 \\ Improvement 5 & & \(\surd\) & \(\surd\) & 91.3 & 13.7 & 7.0 \\ Improvement 6 & \(\surd\) & & \(\surd\) & 91.2 & 15.1 & 4.4 \\ Improvement 7 & \(\surd\) & \(\surd\) & \(\surd\) & 92.2 & 14.0 & 4.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation experiment. essentially the same, but the EMA mechanism effectively improves the detection accuracy and single detection speed. This indicates that the EMA mechanism effectively helps the model to focus on important flame information with greater accuracy, thus improving the performance of the model and reducing redundant calculations. This, in turn, improves the computational efficiency of the model. Improvement 3 concerns the modification of the loss function. This paper employs the NWD loss function, which exhibits superior performance compared to the CIoU loss function. The detection accuracy is notably enhanced, with an improvement of 1.7% compared to the original YOLOv8 network model. The NWD loss function demonstrates enhanced efficacy in detecting minute target objects, exhibiting a greater capacity to capture the object's spatial information and distributional characteristics. Additionally, it exhibits enhanced robustness in detecting scenes with smaller objects. Improvement 4 entails an increase of 0.3 ms in the time required for a single detection and a 0.3 MB increase in the model size in comparison to Improvement 1. Improvement 4 represents a simultaneous improvement to both Improvement 1 and Improvement 2 of the original network. Compared to the original YOLOv8 network, Improvement 4 demonstrates an average increase in accuracy of 1.2%. In comparison to Improvement 1, the average value of accuracy improves by 0.4%. When compared to Improvement 2, the average value of accuracy improves by 0.6%.