Abstract
Fire is one of the mutable hazards that damage properties and destroy forests. Many researchers are involved in early warning systems, which considerably minimize the consequences of fire damage. However, many existing image-based fire detection systems can perform well in a particular field. A general framework is proposed in this paper which works on realistic conditions. This approach filters out image blocks based on thresholds of different temporal and spatial features, starting with dividing the image into blocks and extraction of flames blocks from image foreground and background, and candidates blocks are analyzed to identify local features of color, source immobility, and flame flickering. Each local feature filter resolves different false-positive fire cases. Filtered blocks are further analyzed by global analysis to extract flame texture and flame reflection in surrounding blocks. Sequences of successful detections are buffered by a decision alarm system to reduce errors due to external camera influences. Research algorithms have low computation time. Through a sequence of experiments, the result is consistent with the empirical evidence and shows that the detection rate of the proposed system exceeds previous studies and reduces false alarm rates under various environments.
Highlights
Fire is one of the most uncontrollable phenomena with respect to time and space and directly endangers human life and property and nature
A general framework is proposed in this paper which works on realistic conditions. This approach filters out image blocks based on thresholds of different temporal and spatial features, starting with dividing the image into blocks and extraction of flames blocks from image foreground and background, and candidates blocks are analyzed to identify local features of color, source immobility, and flame flickering
Ko et al [24] proposed a novel fire-flame detection method based on visual features using fuzzy Finite Automata (FFA) and a probability density function
Summary
Fire is one of the most uncontrollable phenomena with respect to time and space and directly endangers human life and property and nature. Ko et al [24] proposed a novel fire-flame detection method based on visual features using fuzzy Finite Automata (FFA) and a probability density function. A novel fire detection system based on the Convolutional Neural Network (CNN) model YOLO v3 (latest variant of a popular object detection algorithm YOLO—You Only Look Once) [27], R–FCN (Region-based Fully Convolutional Network), Faster-RCNN (Region-Based Convolutional Neural Networks) and SSD (single-shot detector) was proposed by Li and Zhao [28]. Classical CNN has high accuracy in a single frame but fails to capture motion information between frames To solve this issue, Hu and Lu [31] proposed a spatial-temporal based CNN algorithm for real-time detection and video smoke detection.
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