UAV-based forest fire detection and tracking using image processing techniques

In this paper, an unmanned aerial vehicle (UAV) based forest fire detection and tracking method is proposed. Firstly, a brief illustration of UAV-based forest fire detection and tracking system is presented. Then, a set of forest fire detection and tracking algorithms are developed including median filtering, color space conversion, Otsu threshold segmentation, morphological operations, and blob counter. The basic idea of the proposed method is to adopt the channel “a” in Lab color model to extract fire-pixels by making use of chromatic features of fire. Numerous experimental validations are carried out, and the experimental results show that the proposed methodology can effectively extract the fire pixels and track the fire zone.

[1]  Chitra Dadkhah,et al.  Automatic fire detection based on soft computing techniques: review from 2000 to 2010 , 2012, Artificial Intelligence Review.

[2]  Luis Merino,et al.  Automatic Forest Fire Monitoring and Measurement using Unmanned Aerial Vehicles , 2010 .

[3]  Ke Xu,et al.  Review of fire detection technologies based on video image , 2013 .

[4]  Rubita Sudirman,et al.  Motion Detection and Analysis with Four Different Detectors , 2011, 2011 Third International Conference on Computational Intelligence, Modelling & Simulation.

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

[6]  Adel Hafiane,et al.  On the Evaluation of Segmentation Methods for Wildland Fire , 2009, ACIVS.

[7]  Aníbal Ollero,et al.  Journal of Intelligent & Robotic Systems manuscript No. (will be inserted by the editor) An Unmanned Aircraft System for Automatic Forest Fire Monitoring and Measurement , 2022 .

[8]  Timothy W. McLain,et al.  Cooperative forest fire surveillance using a team of small unmanned air vehicles , 2006, Int. J. Syst. Sci..

[9]  Chao-Ho Chen,et al.  An early fire-detection method based on image processing , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[10]  Yrjö Neuvo,et al.  Detail-preserving median based filters in image processing , 1994, Pattern Recognit. Lett..

[11]  Ioannis Pitas,et al.  Nonlinear Digital Filters - Principles and Applications , 1990, The Springer International Series in Engineering and Computer Science.

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

[13]  A. Enis Çetin,et al.  Flame detection in video using hidden Markov models , 2005, IEEE International Conference on Image Processing 2005.

[14]  Begoña C. Arrue,et al.  Computer vision techniques for forest fire perception , 2008, Image Vis. Comput..

[15]  Vincent G. Ambrosia,et al.  Selection of Appropriate Class UAS/Sensors to Support Fire Monitoring: Experiences in the United States , 2014 .

[16]  Karolj Skala,et al.  Integrated System For Forest Fire Early Detection and Management , 2008 .

[17]  B. Jiang,et al.  Fault-tolerant shortest connection topology design for formation control , 2014 .

[18]  Youmin Zhang,et al.  A Distributed Deployment Strategy for a Network of Cooperative Autonomous Vehicles , 2015, IEEE Transactions on Control Systems Technology.

[19]  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.

[20]  P. Zarco-Tejada,et al.  REMOTE SENSING OF VEGETATION FROM UAV PLATFORMS USING LIGHTWEIGHT MULTISPECTRAL AND THERMAL IMAGING SENSORS , 2009 .

[21]  Dengyi Zhang,et al.  Forest fire and smoke detection based on video image segmentation , 2007, International Symposium on Multispectral Image Processing and Pattern Recognition.

[22]  Amanpreet Kaur Comparison between YCbCr Color Space and CIELab Color Space for Skin Color Segmentation , 2012 .

[23]  Youmin Zhang,et al.  A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques , 2015 .

[24]  Klamer Schutte,et al.  Autonomous Forest Fire Detection , 1998 .