## 4 Experiments
### Implementation and Results  
#### 4.2.1 Loss Function Choice  
The overall results for deeplabv3+ using MobileNetV2 and Xception within the four different loss functions previously introduced are given in Figures 4 and 5 respectively. All the scores are expressed in percentage (%). For the case of MobileNetV2, the model trained with the Dice loss function draws the higher results, a global accuracy of 98.70%, a mean accuracy of 89.54%, a mean and a weighted IoU of 86.38% and 97.51%, respectively, and a mean BF score of 93.86%. This model is named _Model_1_. In fact, in the model's selection process, the present study focused on the IoU metric more than the BF score even though the first one gives a better idea about the mask correspondence with the ground truth. The IoU and BF scores seem equivalent for a single instance classification; however, averaging the results over an entire dataset is completely different. The IoU gives better insights into the number of misclassified instances, while the BF score gives the pixel-level  
Figure 4: The overall metrics were gathered using different loss functions with Deeplabv3+ with MobileNetV2 as the backbone. Figure 5: The overall metrics were gathered using different loss functions with Deeplabv3+ with Xception as the backbone. squaring error. Hence, the BF score averages while the IoU metric characterizes the worst situation performance. The global and the mean accuracy metrics do not correspond with our expectations of the real segmentation performance for highly imbalanced datasets. Since the BF score mathematical formulation does not consider the number of true negatives (TNs), a large value of TNs will not affect the BF metric. However, for the models trained with Xception, the highest results are recorded also using the Dice loss function. The selected model, named _Model_2_, achieves a global accuracy of 98.57%, a mean accuracy of 90.04%, a mean, and a weighted IoU of 85.47% and 97.29%, respectively, and a mean BF score of 92.34%. The overall results for deeplabv3+ trained with InceptionRestNetV2 within the four different loss functions previously introduced are depicted in Figure 6. The models trained with Tversky and Dice loss functions draw high and approximatively similar mean IoU values. As a result, determining the best model based on these values was challenging. Hence, to describe the behavior of the two models, a deeper investigation of the performance across all datasets was necessary at this stage. The two models testing results over the three sets of data, FLAME, Corsican, and Firefront_Gestosa, are depicted in Figure 7. The chart depicts that the model trained with Dice loss performs better over the two aerial datasets, FLAME and Firefront_Gestosa. Over Corsican data, the model trained with Dice loss performs as well as the one trained with Tversky loss. The main goal of the present work is to create a model that can properly segment fire images captured from aerial datasets with extremely small flame regions. Hence, the present study focused on the performance over the biggest aerial dataset, FLAME, followed by Firefront_Gestosa, with limited flame areas. The model trained with Dice loss and InceptionResNetV2 is named _Model_3_. However, the results for deeplabv3+ trained with RestNet-18 within the four different loss functions previously introduced are depicted in Figure 8. The models trained using the Dice and Cross entropy loss functions produce nearly identical results. Hence, it was crucial to analyze the results individually over every set of data. Figure 6: The overall metrics were gathered using different loss functions with Deeplabv3+ with InceptionResNetV2 as a backbone. The chart of Figure 9 presents the results of the models trained with Dice and Cross entropy loss functions over every data set. We could conclude that the model trained with Dice loss performs substantially better than the others over the two aerial datasets in terms of mean accuracy, mean IoU, and BF score.