A Deep Learning Based Forest Fire Detection Approach Using UAV and YOLOv3

Unmanned aerial vehicles (UAVs) are increasingly being used in forest fire monitoring and detection thanks to their high mobility and ability to cover areas at different altitudes and locations with relatively lower cost. Traditional fire detection algorithms are mostly based on the RGB color model, but their speed and accuracy need further improvements. This paper proposes a forest fire detection algorithm by exploiting YOLOv3 to UAV-based aerial images. Firstly, a UAV platform for the purpose of forest fire detection is developed. Then according to the available computation power of the onboard hardware, a small-scale of convolution neural network (CNN) is implemented with the help of YOLOv3. The testing results show that the recognition rate of this algorithm is about 83%, and the frame rate of detection can reach more than 3.2 fps. This method has great advantages for real-time forest fire detection application using UAVs.

[1]  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 .

[2]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

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

[5]  Vito Cappellini,et al.  An intelligent system for automatic fire detection in forests , 1989, Recent Issues in Pattern Analysis and Recognition.

[6]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[8]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[9]  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.

[10]  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.

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

[12]  Anis Koubaa,et al.  DroneTrack: Cloud-Based Real-Time Object Tracking Using Unmanned Aerial Vehicles Over the Internet , 2018, IEEE Access.

[13]  Tariq Abdullah,et al.  Development of a Low Cost and Light Weight UAV for Photogrammetry and Precision Land Mapping Using Aerial Imagery , 2016, 2016 International Conference on Frontiers of Information Technology (FIT).

[14]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Youmin Zhang,et al.  UAV-based forest fire detection and tracking using image processing techniques , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[16]  Qihui Wu,et al.  An Amateur Drone Surveillance System Based on the Cognitive Internet of Things , 2017, IEEE Communications Magazine.

[17]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  J. Everaerts,et al.  THE USE OF UNMANNED AERIAL VEHICLES ( UAVS ) FOR REMOTE SENSING , 2008 .

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