Automated Fire Extinguishing System Using a Deep Learning Based Framework

Abstract

Fire accidents occur in every part of the world and cause a large number of casualties because of the risks involved in manually extinguishing the fire. In most cases, humans cannot detect and extinguish fire manually. Fire extinguishing robots with sophisticated functionalities are being rapidly developed nowadays, and most of these systems use fire sensors and detectors. However, they lack mechanisms for the early detection of fire, in case of casualties. To detect and prevent such fire accidents in its early stages, a deep learning-based automatic fire extinguishing mechanism was introduced in this work. Fire detection and human presence in fire locations were carried out using convolution neural networks (CNNs), configured to operate on the chosen fire dataset. For fire detection, a custom learning network was formed by tweaking the layer parameters of CNN for detecting fires with better accuracy. For human detection, Alex-net architecture was employed to detect the presence of humans in the fire accident zone. We experimented and analyzed the proposed model using various optimizers, activation functions, and learning rates, based on the accuracy and loss metrics generated for the chosen fire dataset. The best combination of neural network parameters was evaluated from the model configured with an Adam optimizer and softmax activation, driven with a learning rate of 0.001, providing better accuracy for the learning model. Finally, the experiments were tested using a mobile robotic system by configuring them in automatic and wireless control modes. In automatic mode, the robot was made to patrol around and monitor for fire casualties and fire accidents. It automatically extinguished the fire using the learned features triggered through the developed model.