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

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.

[1]  Gurjit Singh Walia,et al.  Intelligent fire-detection model using statistical color models data fusion with Dezert–Smarandache method , 2013 .

[2]  Dengyi Zhang,et al.  SVM based forest fire detection using static and dynamic features , 2011, Comput. Sci. Inf. Syst..

[3]  ByoungChul Ko,et al.  Modeling and Formalization of Fuzzy Finite Automata for Detection of Irregular Fire Flames , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Abhishek Kumar Singh,et al.  Video Flame and Smoke Based Fire Detection Algorithms: A Literature Review , 2020, Fire Technology.

[5]  Wentao Mao,et al.  Fire Recognition Based On Multi-Channel Convolutional Neural Network , 2018 .

[6]  Hao Wu,et al.  An intelligent fire detection approach through cameras based on computer vision methods , 2019, Process Safety and Environmental Protection.

[7]  Shusen Cheng,et al.  Image-Based Flame Detection and Combustion Analysis for Blast Furnace Raceway , 2019, IEEE Transactions on Instrumentation and Measurement.

[8]  Ebroul Izquierdo,et al.  A Probabilistic Approach for Vision-Based Fire Detection in Videos , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Hasan Demirel,et al.  Fire detection in video sequences using a generic color model , 2009 .

[10]  A. Enis Çetin,et al.  Computer vision based method for real-time fire and flame detection , 2006, Pattern Recognit. Lett..

[11]  Sung Wook Baik,et al.  Early fire detection using convolutional neural networks during surveillance for effective disaster management , 2017, Neurocomputing.

[12]  Pu Li,et al.  Image fire detection algorithms based on convolutional neural networks , 2020, Case Studies in Thermal Engineering.

[13]  Carlton R. Pennypacker,et al.  Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images , 2020, Remote. Sens..

[14]  Bing Zhang,et al.  Flame Recognition in Video Images with Color and Dynamic Features of Flames , 2019 .

[15]  Xiaobo Lu,et al.  Real-time video fire smoke detection by utilizing spatial-temporal ConvNet features , 2018, Multimedia Tools and Applications.

[16]  Osman Günay,et al.  Fire Detection in Video Using LMS Based Active Learning , 2010 .

[17]  Nikolaos Grammalidis,et al.  Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection , 2015, IEEE Transactions on Circuits and Systems for Video Technology.