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

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.

Unsupervised Method for Wildfire Flame Segmentation and Detection

“…In Hsu et al [12] (2020), a flame color analysis method is proposed based on modified rules presented in [11]. The authors observed that [11] performs well for low-intensity fires, where dominant colors are red and yellow.…”

“…When the background noise is successfully removed, the method for extraction of flame pixel might require less restrictive rules. Some examples of color spaces that are used in the literature are RGB [10], YCbCr [11] [12], and CIELab [13]. Other methods combine different color spaces to increase the chance of detection of flame pixels, such as the methods that combine HSV, HSL, and HWB [14], and RGB and YCbCr [15] color spaces.…”

Frame Difference Method to Detect Fire and Compare the Accuracy and Precision with Vibe Method

“…The research method has less computational time. Still the algorithm shows more false alarm rate which is caused by the shaking of camera (Hsu et al 2020).The optical remoting sensing system is used in the fire warning system to alert the people who are living nearby the forest. This system depends on the camera resolution and view of the angel.…”

Hybrid Feature Fusion-Based High-Sensitivity Fire Detection and Early Warning for Intelligent Building Systems

“…Although vision-based sensing systems have shown good performance, massive and complex datasets often delay the time in the decision-making process [ 17 ]. Thus, visual-based sensing systems are not appropriate for early fire detection where decision making needs to be conducted in limited time.…”