Machine Vision Based Fire Detection Techniques: A Survey

Abstract

The risk of fires is ever increasing along with the boom of urban buildings. The current methods of detecting fire with the use of smoke sensors with large areas, however poses an issue. The introduction of video surveillance systems has given a great opportunity for identifying smoke and flame from faraway locations and tackles this risk. Processing this huge amount of data is a problem with using these video and image data. In recent times, a number of methods have been proposed to deal with this challenge and identify fire and smoke. Image processing algorithms for detecting flame and smoke, motion-based estimation of smoke, etc are some of the methods that are proposed earlier. Recently, there has been an array of methods proposed using Deep Learning, Convolutional Neural Networks (CNNs) to automatically detect and predict flame and smoke in videos and images. In this paper, we present a complete survey and analysis of these machine vision based fire/smoke detection methods and their performance. Firstly, we introduce the fundamentals of image processing methods, CNNs and their application prospect in video smoke and fire detection. Next, the existing datasets and summary of the recent methodologies used in this field are discussed. Finally, the challenges and suggested improvements to further the development of the application of CNNs in this field are discussed. CNNs are shown to have a great potential for smoke and fire detection and better development can help prepare a robust system that would greatly save human lives and monetary wealth from getting destroyed from fires. Finally, research guidelines are presented to fellow researchers regarding data augmentation, fire and smoke detection models which need to be investigated in the future to make progress in this crucial area of research.