Video Flame and Smoke Based Fire Detection Algorithms: A Literature Review

Abstract

This review is focused on video flame and smoke based fire detection algorithms for both indoor and outdoor environments. It analyzes and discusses them in a taxonomical manner for the last two decades. These are mainly based on handcraft features with or without classifiers and deep learning approaches. The separate treatment is provided for detecting flames and smoke. Their static and dynamic characteristics are elaborated for the handcraft feature approach. The blending of the obtained features from these characteristics is the focus of most of the research and these concepts are analyzed critically. A fusion of both visible and thermal images leading to multi-fusion and multimodal approaches have conversed. It is a step towards obtaining accurate detection results and how the handcraft feature approach tackles the problems of flame and smoke detection, as well as their weaknesses are discussed which are still not solved. Some of these weaknesses can be tackled by developing a technology based on artificial intelligence named deep-learning. Its taxonomical literature study with a focus on the flame and smoke detection is presented. The strengths and weaknesses of this approach are discussed with possible solutions. The latest trend in literature which focuses on the hybrid approach utilizing both handcraft feature, and deep learning approaches is discussed. This approach aims to minimize the weaknesses still present in the current systems.

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

“…Recent advances in computer vision, machine learning, and remote sensing technologies offer new tools for detecting and monitoring forest fires, while the development of new materials and microelectronics have allowed sensors to be more efficient in identifying active forest fires. Unlike other fire detection review papers that have focused on various sensing technologies [ 5 ], on video flame or/and smoke methodologies in visible or/and InfraRed (IR) range [ 6 , 7 , 8 , 9 ], on various environments [ 10 ], and airborne systems [ 11 , 12 ], in this paper, we provide a comprehensive study of the most representative forest fire detection systems, focusing on those that use optical remote sensing, as well as digital image processing [ 13 ] and classification techniques [ 14 ]. Depending on the acquisition level, three broad categories of widely used systems that can detect or monitor active fire or smoke incidents in real/near-real-time are identified and discussed, namely terrestrial, aerial, and satellite.…”

An Early Flame Detection System Based on Image Block Threshold Selection Using Knowledge of Local and Global Feature Analysis

“…The system is capable of handling continuous spaces by combining methodologies of automata and fuzzy logic. Gaur et al [25] described recent developments in the area of fire detection using video sequence. Higher detection rates are targeted by most of the literature published.…”

Wildfire Segmentation Using Deep Vision Transformers

“…DL approaches are used in forest fire segmentation tasks to extract the geometrical characteristics of the fire, such as height, width, angle, and so forth. These models, especially Convolutional Neural Networks (ConvNets), were also successfully employed to predict and detect the boundaries of fire as well as to identify and segment each fire pixel [7,8]. Their impressive results help to develop metrology tools, which can be used in modeling fire preparation as well as providing the necessary inputs to the mathematical propagation models.…”