Image Processing Based Forest Fire Detection using Infrared Camera

When time goes by, human beings are advancing in technology, artificial and natural disasters are drastically increasing. The forest fire is one of the hazards. Forest fire incinerates trees that provide us with oxygen and if it is not detected early, it is very elusive to stop a forest fire from continue burns. The project’s objective is to capture infrared image of forest fire detection using the appropriate camera, detect fire with RGB and YCbCr colour model to isolate fire pixels from the background and separate luminance and chrominance from the original image, and filter image using MATLAB Analyzer to process images. The method is tested on a selected image, which captured by the camera that contains fire. Next method is used for calculating and analysing the fire image, which to differentiate between fire detection or false detection. Other method is used to process the fire image, which the image will compute and shown in terminal nodes and graphs by using Wavelet Analyzer 5.0. The results of this system are achieved fire detection and obtain data for the fire images.

[1]  Ole-Christoffer Granmo,et al.  A methodology for fire data analysis based on pattern recognition towards the disaster management , 2015, 2015 2nd International Conference on Information and Communication Technologies for Disaster Management (ICT-DM).

[2]  Thilo Kahl,et al.  Towards Improved Airborne Fire Detection Systems Using Beetle Inspired Infrared Detection and Fire Searching Strategies , 2015, Micromachines.

[3]  Yiran Chen,et al.  Learning Structured Sparsity in Deep Neural Networks , 2016, NIPS.

[4]  Wen-Bing Horng,et al.  A new image-based real-time flame detection method using color analysis , 2005, Proceedings. 2005 IEEE Networking, Sensing and Control, 2005..

[5]  Mubarak Shah,et al.  Flame recognition in video , 2000, Proceedings Fifth IEEE Workshop on Applications of Computer Vision.

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

[7]  W. Robertson,et al.  Comparing audio compression using wavelets with other audio compression schemes , 1999, Engineering Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411).

[8]  Zhu Shi,et al.  Flame Oscillation Frequency Based on Image Correlation , 2008 .

[9]  Tsong-Yi Chen,et al.  Smoke Detection for Early Fire-Alarming System Based on Video Processing , 2008, J. Digit. Inf. Manag..

[10]  Haidi Ibrahim,et al.  Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement , 2007, IEEE Transactions on Consumer Electronics.

[11]  Siau-Chuin Liew,et al.  Fire Detection Algorithm using Image Processing Techniques , 2015 .

[12]  Turgay Çelik,et al.  Fire Pixel Classification using Fuzzy Logic and Statistical Color Model , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[13]  N. Kingsbury Complex Wavelets for Shift Invariant Analysis and Filtering of Signals , 2001 .

[14]  Hong Cheng,et al.  Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network , 2016, Sensors.

[15]  Héctor M. Pérez Meana,et al.  An Early Fire Detection Algorithm Using IP Cameras , 2012, Sensors.

[16]  Chao-Ho Chen,et al.  The smoke detection for early fire-alarming system base on video processing , 2006, 2006 International Conference on Intelligent Information Hiding and Multimedia.

[17]  Alejandro Gutiérrez-Giles,et al.  GPI based velocity/force observer design for robot manipulators. , 2014, ISA transactions.

[18]  Mubarak Shah,et al.  Flame recognition in video , 2002, Pattern Recognit. Lett..