Automated Fire Detection System Using Image Surveillance System (ISS) and Convolutional Neural Networks (CNN)

Fire has become more complicated for places such as forests, malls, industries, functions with huge crowds, and theaters. Fires cause massive losses to all areas every year. There are a lot of severe threats to the people, assets, environment, and human and animal life with these existing fire safety methods. Detecting the fire in the early stages helps prevent the loss of people, properties, plants, etc. In some places they will use the smoke detectors to find the seriousness of the fire. Deep learning algorithms are most widely used to find the abnormalities, such as firing at specific places. Many existing models can be used to find accurate fire detection using various online sources such as images and videos. Various limitations of existing models are identified that will reduce the accuracy. This paper describes the new fire detection model that detects fire at any place in the early stages. The proposed automated model combined with a high-end image surveillance system (ISS) and convolutional neural networks (CNN) achieved better accuracy of fire detection in images. Bounding box is one of the models that find the fire affected regions from the dataset input images. Evaluation results show that the performance of proposed system achieved the better results.