# LW-FIRE: A Lightweight Wildfire Image Classification with a Deep Convolutional Neural Network
## 5 Discussion
\begin{table}
\begin{tabular}{c l c c c c c c c} \hline \hline \# & **Model** & **TPR** & **FPR** & **TNR** & **PPV** & **ACC** & **F1** & **MCC** & **FM** \\ \hline \#1 & FireNet [6] & 0.846 & 0.0831 & 0.916 & 0.910 & 0.881 & 0.877 & 0.765 & 0.878 \\ \hline \#2 & InceptionNetV1-OnFire [6] & 0.810 & 0.0666 & 0.933 & 0.924 & 0.871 & 0.863 & 0.749 & 0.865 \\ \hline \#3 & InceptionNetV3-OnFire [7] & 0.949 & 0.078 & 0.9218 & 0.9239 & 0.9358 & 0.9367 & 0.872 & 0.936 \\ \hline \#4 & InceptionNetV4-OnFire [7] & 0.9481 & **0.0477** & **0.952** & **0.952** & **0.950** & **0.950** & **0.900** & **0.950** \\ \hline \#5 & NasNet-A-OnFire [8] & 0.973 & 0.082 & 0.917 & 0.922 & 0.945 & 0.947 & 0.892 & 0.947 \\ \hline \#6 & ShuffleNetV2-OnFire [8] & 0.960 & 0.078 & 0.921 & 0.9247 & 0.941 & 0.942 & 0.883 & 0.942 \\ \hline \#8 & **Our LW-FIRE15042 * & **0.980** & 0.091 & 0.908 & 0.914 & 0.944 & 0.946 & 0.891 & 0.946 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison with the state of the art on the dataset presented in [6]. Our model was not trained on this dataset, though other models have been trained and tested on this dataset. * Evaluated on \(2\times 2\) tiles of images in this dataset (as presented in Figure 8). \begin{table}
\begin{tabular}{c l c c c c c c c c} \hline \hline \# & **Model** & **TPR** & **FPR** & **TNR** & **PPV** & **ACC** & **F1** & **MCC** & **FM** \\ \hline \#1 & FireNet [6] & 0.727 & 0.0559 & 0.944 & 0.928 & 0.835 & 0.816 & 0.688 & 0.8226 \\ \hline \#2 & InceptionNetV1-OnFire [6] & 0.708 & 0.055 & 0.944 & 0.926 & 0.826 & 0.803 & 0.671 & 0.810 \\ \hline \#3 & InceptionNetV3-OnFire [7] & 0.794 & 0.028 & 0.971 & 0.964 & 0.882 & 0.871 & 0.777 & 0.875 \\ \hline \#4 & InceptionNetV4-OnFire [7] & 0.869 & 0.036 & 0.963 & 0.959 & 0.916 & 0.912 & 0.836 & 0.913 \\ \hline \#5 & NasNet-A-OnFire [8] & 0.820 & 0.0637 & 0.936 & 0.927 & 0.878 & 0.870 & 0.761 & 0.872 \\ \hline \#6 & ShuffleNetV2-OnFire [8] & 0.820 & **0.025** & **0.974** & **0.970** & 0.897 & 0.889 & 0.804 & 0.892 \\ \hline \#7 & **Our RT-LW-FIRE10012** & **0.966** & 0.063 & 0.936 & 0.938 & **0.951** & **0.952** & **0.903** & **0.952** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with the state of the art on the dataset presented in this paper (\(N=12\)). \begin{table}
\begin{tabular}{c l c c c c c c c} \hline \hline \# & **Model** & **TPR** & **FPR** & **TNR** & **PPV** & **ACC** & **F1** & **MCC** & **FM** \\ \hline \#1 & FireNet [6] & 0.695 & 0.039 & 0.960 & 0.946 & 0.827 & 0.801 & 0.679 & 0.810 \\ \hline \#2 & InceptionNetV1-OnFire [6] & 0.598 & 0.020 & 0.979 & 0.966 & 0.788 & 0.738 & 0.624 & 0.760 \\ \hline \#3 & InceptionNetV3-OnFire [7] & 0.769 & 0.030 & 0.969 & 0.961 & 0.869 & 0.854 & 0.753 & 0.860 \\ \hline \#4 & InceptionNetV4-OnFire [7] & 0.753 & 0.0328 & 0.967 & 0.958 & 0.860 & 0.843 & 0.737 & 0.849 \\ \hline \#5 & NasNet-A-OnFire [8] & 0.730 & **0.009** & **0.990** & **0.987** & 0.860 & 0.839 & 0.746 & 0.849 \\ \hline \#6 & ShuffleNetV2-OnFire [8] & 0.791 & 0.013 & 0.986 & 0.983 & 0.889 & 0.877 & 0.793 & 0.882 \\ \hline \#8 & **Our LW-FIRE15042** & **0.976** & 0.030 & 0.969 & 0.970 & **0.973** & **0.973** & **0.946** & **0.973** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with the state of the art on the dataset presented in this paper (\(N=42\)). fire. In these examples, our model (B) correctly classified sub-images as \"fire\" or \"not-fire\" images. However, it misclassified sub-images in Video 3, where both algorithms have shown bad classification results. The results of the first frame of Video 3, 5, and 6, and the last frame of Video 1 and Video 5 show identical results of both models. On this statistical sample, LW-FIRE150\({}_{42}\) performed better, and it showed better performance in the overall results in this experiment, which is also reflected in Table 6.  
In Experiment II, we tested all models on the dataset described in this paper, where \(N=12\) and \(N=42\).