# Laboratory Flame Smoke Detection Based on an Improved YOLOX Algorithm
## 3 Results
### Analysis of Test Results of Different Models  
The improved YOLOX model was compared to five other models on the same dataset used in this paper to assess how well it performed at detecting flames and smoke. The evaluation indices of all the algorithms were totaled, and the results of the comparison experiments are shown in Table 4 and Figure 12 (PR curves for flame and smoke, respectively). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Swin-Transformer** & **CBAM** & **Slim Neck** & **Loss Value** \\ \hline YOLOX & \(\times\) & \(\times\) & \(\times\) & 3.66 \\ Improvement1 & \(\boldsymbol{\mathscr{V}}\) & \(\times\) & \(\times\) & 3.05 \\ Improvement2 & \(\times\) & \(\boldsymbol{\mathscr{V}}\) & \(\times\) & 3.60 \\ Improvement3 & \(\times\) & \(\times\) & \(\boldsymbol{\mathscr{V}}\) & 3.49 \\ Improvement4 & \(\boldsymbol{\mathscr{V}}\) & \(\times\) & \(\times\) & 2.94 \\ Improvement5 & \(\boldsymbol{\mathscr{V}}\) & \(\times\) & \(\boldsymbol{\mathscr{V}}\) & 2.88 \\ Improvement6 & \(\boldsymbol{\mathscr{V}}\) & \(\boldsymbol{\mathscr{V}}\) & \(\times\) & 2.91 \\ Improvement7 & \(\boldsymbol{\mathscr{V}}\) & \(\boldsymbol{\mathscr{V}}\) & \(\boldsymbol{\mathscr{V}}\) & 2.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Final training loss function values for different improvement methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Swin-Transformer** & **CBAM** & **Slim Neck** & **mAPtest** \\ \hline YOLOX & \(\times\) & \(\times\) & \(\times\) & 86.17\% \\ Improvement1 & \(\boldsymbol{\mathscr{V}}\) & \(\times\) & \(\times\) & 90.52\% \\ Improvement2 & \(\times\) & \(\boldsymbol{\mathscr{V}}\) & \(\times\) & 87.13\% \\ Improvement3 & \(\times\) & \(\times\) & \(\boldsymbol{\mathscr{V}}\) & 86.30\% \\ Improvement4 & \(\boldsymbol{\mathscr{V}}\) & \(\boldsymbol{\mathscr{V}}\) & \(\times\) & 91.48\% \\ Improvement5 & \(\boldsymbol{\mathscr{V}}\) & \(\times\) & \(\boldsymbol{\mathscr{V}}\) & 90.72\% \\ Improvement6 & \(\boldsymbol{\mathscr{V}}\) & \(\boldsymbol{\mathscr{V}}\) & \(\times\) & 91.48\% \\ Improvement7 & \(\boldsymbol{\mathscr{V}}\) & \(\boldsymbol{\mathscr{V}}\) & \(\boldsymbol{\mathscr{V}}\) & 92.26\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of mAP for ablation experiments. Figure 12: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Models** & **fireF1** & **smokeF1** & **fireP** & **smokeP** & **fireR** & **smokeR** & **fireAP** & **smokeAP** & **mAP** \\ \hline SSD & 74\% & 61\% & 85.05\% & 81.40\% & 65.29\% & 48.99\% & 77.37\% & 64.82\% & 71.09\% \\ Faster R-CNN & 82\% & 74\% & 86.28\% & 81.32\% & 77.85\% & 67.89\% & 85.40\% & 78.76\% & 82.08\% \\ YOLOv4 & 76\% & 67\% & 87.20\% & 80.99\% & 67.80\% & 57.06\% & 78.96\% & 70.90\% & 74.93\% \\ YOLOv5 & 81\% & 71\% & 87.13\% & 83.83\% & 76.50\% & 61.83\% & 84.44\% & 76.93\% & 80.68\% \\ YOLOX & 85\% & 77\% & 87.52\% & 81.80\% & 82.42\% & 73.39\% & 88.65\% & 83.68\% & 86.17\% \\ Improvement YOLOX & 89\% & 87\% & 90.99\% & 87.78\% & 87.89\% & 86.97\% & 92.78\% & 92.46\% & 92.26\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of evaluation indexes of different models. Figure 12: Cont. When compared to the original YOLOX model, SSD model, Faster R-CNN model, YOLOv4 model, and YOLOv5 model, the revised YOLOX model has varying degrees of performance improvement, according to the results in Table 3 and Figure 12. The upgraded YOLOX model exhibits mAP improvements of 21.17% compared to SSD, 10.08% compared to Faster R-CNN, 17.33% compared to YOLOv4, 11.58% compared to YOLOv5, and 6.09% compared to the original YOLOX, showing that the improved YOLOX utilizing three different techniques has superior performance. In terms of accuracy and recall, the F1 score can address the issue of a one-sided evaluation of the model and allow for a more thorough review.