Hardware Implementation of Forest Fire Detection System using Deep Learning Architectures

Abstract

Forests being called as lungs of earth play a very important role in maintaining a sustainable climate on the earth. They are instrumental in maintaining a quality eco-system by filtering the air, preventing soil erosion and help to maintain diverse life on the earth. Forest fires are a matter of concern in terms of economic growth and ecological damage and damage to animals and human life. Forest fires contribute to global warming and imbalances the climate on the earth making the lives harder. Early detection of forest fire can prevent the damage by a great extent. Sensor based and Image processing-based methods have been widely used followed by machine learning techniques to process the sensor data and detect the occurrence of forest fires. These methods are costly and difficult to install at different locations in the forest. As the dimensions of the forest area increases, the complexity of the system also increases. Deep Learning techniques such as variations of convolutional neural networks process image data and can provide an early warning about the occurrence of the fire. In the proposed system different pre trained deep neural network architectures such as Resnet 50, InceptionV3, GoogleNet, AlexNet, MobileNet have been employed using transfer learning approaches on two very important datasets namely Mendely dataset and Kaggle Datasets. The best performing architecture i.e Alexnet has been deployed on to Raspberry PI embedded hardware to work as a standalone module. The trained models have demonstrated a good accuracy of 99.45% on Mendely and 99.42 on Kaggle Datasets for Fire detection.