## 4 Experiments
### Implementation and Results
#### 4.2.2 Model's Comparison  
Table 5 shows the overall performance of the five selected models while Table 6 introduces the computational cost of every model in terms of training and test time per dataset.  
Figure 11: The overall metrics, over the three sets of data (FLAME, Corsican, and Firefront_Gestosa), were gathered using Tversky and Dice loss functions with Deeplabv3+ with ResNet-50 as a backbone.  
It can be observed from the overall results depicted in Table 5 that ResNet-50 (Model_5) achieves the best performance with a global accuracy of 98.65%, a mean accuracy of 90.86%, a mean IoU 86.27%, a weighted IoU of 97.44%, and a mean BF score of 93.63%.  
MobileNetV2 (Model_1) attains competitive performance, with a smaller training duration (average training time 55 min and 10 s), as shown in Table 6, in comparison with ResNet-50 (Model_5). Model_5 draws a global accuracy of 98.70%, a mean accuracy of 89.54%, a mean IoU 86.38%, a weighted IoU of 97.51%, and a mean BF score of 93.86%. The ResNet-50 had required an average training time of 1 h, 5 min, and 34 s.  
The InceptionResNetV2 (Model_3) and the Xception (Model_2) architectures draw relatively lower performance since the first focuses on computational cost and the second, as an extension of the first, reframes the same network concept.  
It is worth noting that the Xception substitutes the original Inception modules with depth-wise separable convolutions. InceptionResNetV2, as expected, required maximum training time (average 1 h, 29 min, and 20 s) as it is one of the most in-depth networks used in our study.  
We note that it is normal that the average detection time over Firefront_Gestosa is relatively higher since the size of original images is bigger than FLAME and Corsican.  
The performance of first and fifth models are highlighted in Table 7 for better insight. It is evident that the Model_5 outperforms Model_1 based on performance comparison. The Model_5 draws higher results over Flame data, a global accuracy of 99.79%, a mean accuracy of 92.02% a mean IoU 84.74%, a weighted IoU of 99.61%, and a mean BF score of 96.43%. Alongside, Model_1 draws a very low mean accuracy and mean BF score of 87.81% and 95.66% respectively, in comparison to Model_5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{
\begin{tabular}{c} **Average Training Time per** \\ **Network (Hour:Minute:Second)** \\ \end{tabular} } & \multicolumn{2}{c}{**Average Detection Time per Image (Second)**} \\ \cline{3-5}  & & **FLAME** & **Corsican** & **Firefront\_Gestosa** \\ \hline Model\_1 & **00:55:10** & **1.0181** & **0.6178** & **0.8745** \\ \hline Model\_2 & 00:47:21 & 1.2033 & 0.68 & 1.0016 \\ \hline Model\_3 & 01:29:20 & 7.0613 & 2.4511 & 4.9538 \\ \hline Model\_4 & 00:47:59 & 0.9122 & 0.8375 & 0.7407 \\ \hline Model\_5 & 01:05:However, Model_5 attains a global accuracy of 99.97%, a mean accuracy of 92.61%, a mean IoU 79.09%, a weighted IoU of 99.95%, and a mean BF score of 98.60% over Firefront_Gestosa. Besides, Model_1 attains a higher mean IoU of 80,13% but a very low mean accuracy of 89.01% compared to Model_5. For the case of Corsican data, the Model_1 gives a higher mean Bf score.  
Nonetheless, the computational cost of Model_5 is higher than Model_1. Notwithstanding that Model_5 consumes higher training resources; the detection time is the same as Model_1 (please refer to Table 6).