Fire Detection using Deeplabv3+ with Mobilenetv2

Fire detection is high priority task in the current decade, due to the high occurrences of fire in urban and forest area. Every year, millions of hectares of forests are burned and destroyed. The cost of dislocation could be optimized by implementing an accurate detection system. In this paper a Deeplabv3+ model with a Mobilenetv2 backbone is implemented and tested over R GB and Infrared pictures of the Corsican french dataset. Three different types of loss function were used to overcome the problem of unbalanced dataset. The results obtained with the model herein presented are very encouraging.