Fire segmentation using a SqueezeSegv2

Abstract

In the last decade, the limitation of the propagation of Wildfire had become a higher necessity. In fact, it is important to optimize the resources used for dislocation to verify the probabilistic signaled fire zones. Hence, using sophisticated and low-cost techniques to sense the previously mentioned zones is highly demanded. Models with high computational necessity are not interesting for real time application. More simple models are requested, to fulfill the desired tasks with an admitted response time. Squeezesegv2 is a model applied initially for LiDAR (Light Detection And Ranging) Point Cloud data segmentation, which gives a high IoU value compared with other state of art architectures. The model was experimented in this paper, it is robust against dropout noise. Experiments were run over RGB pictures of Corsican public French dataset with 1135 RGB images. It is common that highly unbalanced datasets, which is our case, induce high precision low sensitivity. Therefore, several validation measures criterions were adopted to access the performance. In fact, the capability of the model was tested with four different metrics: Accuracy, mean Intersection over Union (IoU), Mean Boundary F1 (BF) Score, and Mean Dice coefficient. The experimental results demonstrate that the trained model, over the Corsican French dataset, with five-fold cross validation procedure can accurately detect the fire flame. The results were collected for different loss function types: Focal loss, Dice and Tversky loss. In general, the given results are very encouraging for further study using deep learning approaches.