Deep Learning-Based Fire Detection for Enhanced Safety Systems

Abstract

Fire detection systems are a critical aspect of modern safety and security systems, playing a pivotal role in safeguarding lives and property against the destructive force of fires. Rapid and accurate identification of fire incidents is essential for timely response and mitigation efforts. Traditional fire detection methods have made substantial advancements, but with the advent of computer vision technologies, the field has witnessed a transformative shift. This paper presents a method for fire detection using deep convolutional neural network (CNN) models. This approach used transfer learning by employing two pre-trained CNN models from the ImageNet dataset: VGG (Visual Geometry Group) and InceptionV3 to extract valuable features from input images. Then, these extracted features serve as input for a machine learning (ML) classifier, namely the Softmax classifier. The Softmax activation function computes the probability distribution to assign accurate class probabilities for discriminating between two types of images: fire and non-fire. Experimental results showed that the proposed method successfully detected fire areas and achieved seamless classification performance compared to other current fire detection methods.