Aerial Forest Fire Surveillance - Evaluation of Forest Fire Detection Model using Aerial Videos

Unmanned Aerial Vehicles (UAVs), which can provide an aerial view for fast responding in large-scale zones of disaster, are recently utilized for forest fire monitoring. In this paper, one general model of forest fire detection using aerial videos is investigated to prove its robustness for practical application of aerial forest fire surveillance. Fire pixels are extracted using the color and motion characteristics of fire. The fire detection performance is evaluated through a large database of various scene conditions to show the efficiency as well as deficiency of our fire detection model in previous study. Our database consists of 49 aerial videos with total of 16898 examined frames of forest fires. The accuracy rate of our forest fire detection model is 93.97 % while the false alarm rate and the miss rate are 7.08% and 6.86 %, respectively. Thick smoke which covers almost the fire is found as the main cause of miss detection in our fire detection model. To enhance the detection performance, in this study we propose one more stage of smoke detection. Smoke pixels are segmented using both color and motion characteristics of smoke. The results prove that smoke detection stage give help in detecting the fire area in case of smoke.

[1]  Xi Zhang,et al.  A real-time video fire flame and smoke detection algorithm , 2013 .

[2]  Junguo Zhang,et al.  Forest fire detection system based on wireless sensor network , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[3]  Martin Mueller,et al.  Optical Flow Estimation for Flame Detection in Videos , 2013, IEEE Transactions on Image Processing.

[4]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[5]  SMOKE DETECTION BASED ON IMAGE PROCESSING BY USING GREY AND TRANSPARENCY FEATURES , 2018 .

[6]  Abolfazl Razi,et al.  Wildfire Monitoring in Remote Areas using Autonomous Unmanned Aerial Vehicles , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[7]  Lokman Nor Anis Aneza,et al.  FIRE RECOGNITION USING RGB AND YCBCR COLOR SPACE , 2015 .

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

[9]  Jian Wang,et al.  Video smoke detection using shape, color and dynamic features , 2017, J. Intell. Fuzzy Syst..

[10]  S. S. Vinsley,et al.  Image processing based forest fire detection using YCbCr colour model , 2014, 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014].

[11]  Gwanggil Jeon,et al.  Vision-Based Fire Detection Algorithm Using Optical Flow , 2012, 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems.

[12]  Thou-Ho Chen,et al.  An intelligent real-time fire-detection method based on video processing , 2003, IEEE 37th Annual 2003 International Carnahan Conference onSecurity Technology, 2003. Proceedings..

[13]  Hanh Dang-Ngoc,et al.  Evaluation of Forest Fire Detection Model using Video captured by UAVs , 2019, 2019 19th International Symposium on Communications and Information Technologies (ISCIT).

[14]  Youmin Zhang,et al.  Aerial Images-Based Forest Fire Detection for Firefighting Using Optical Remote Sensing Techniques and Unmanned Aerial Vehicles , 2017, Journal of Intelligent & Robotic Systems.

[15]  Sadko Mandzuka,et al.  The use of Unmanned Aerial Vehicles for forest fire monitoring , 2016, 2016 International Symposium ELMAR.

[16]  Ahmad A A Alkhatib.  Smart and Low Cost Technique for Forest Fire Detection using Wireless Sensor Network , 2013 .

[17]  Sunita Harlapur,et al.  Fire Detection using Optical Flow Method in Videos , 2015 .

[18]  N. Brovko,et al.  An Efficient Smoke Detection Algorithm for Video Surveillance Systems Based on Optical Flow , 2012 .

[19]  Youmin Zhang,et al.  Fire detection using infrared images for UAV-based forest fire surveillance , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[20]  Ricardus Anggi Pramunendar,et al.  Multi color feature, background subtraction and time frame selection for fire detection , 2013, 2013 International Conference on Robotics, Biomimetics, Intelligent Computational Systems.

[21]  Youmin Zhang,et al.  Unmanned aerial vehicle based forest fire monitoring and detection using image processing technique , 2016, 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC).

[22]  Youmin Zhang,et al.  Vision-based forest fire detection in aerial images for firefighting using UAVs , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[23]  Ahmad A. A. Alkhatib.  A Review on Forest Fire Detection Techniques , 2014, Int. J. Distributed Sens. Networks.

[24]  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..