A hybrid deep learning model by combining convolutional neural network and recurrent neural network to detect forest fire

Abstract

Forest fire poses a serious threat to wildlife, environment, and all mankind. This threat has prompted the development of various intelligent and computer vision based systems to detect forest fire. This article proposes a novel hybrid deep learning model to detect forest fire. This model uses a combination of convolutional neural network (CNN) and recurrent neural network (RNN) for feature extraction and two fully connected layers for final classification. The final feature map obtained from the CNN has been flattened and then fed as an input to the RNN. CNN extracts various low level as well as high level features, whereas RNN extracts various dependent and sequential features. The use of both CNN and RNN for feature extraction is proposed in this article for the first time in the literature of forest fire detection. The performance of the proposed system has been evaluated on two publicly available fire datasets—Mivia lab dataset and Kaggle fire dataset. Experimental results demonstrate that the proposed model is able to achieve very high classification accuracy and outperforms the existing state-of-the-art results in this regard.