A new algorithm for forest fire smoke detection based on MODIS data in Heilongjiang province

2011 ◽  
Author(s):  
Jing Wang ◽  
Weiguo Song ◽  
Wei Wang ◽  
Yongming Zhang ◽  
Shixing Liu
Keyword(s):  
Forest Fire ◽  
Heilongjiang Province ◽  
Smoke Detection ◽  
Modis Data ◽  
Fire Smoke

scholarly journals Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data

2015 ◽  
Vol 7 (4) ◽  
pp. 4473-4498 ◽  
Author(s):  
Xiaolian Li ◽  
Weiguo Song ◽  
Liping Lian ◽  
Xiaoge Wei
Keyword(s):  
Neural Network ◽  
Forest Fire ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Smoke Detection ◽  
Modis Data ◽  
Fire Smoke

A High-precision Forest Fire Smoke Detection Approach Based on DRGNet to Remote Sensing Through Uavs

2021 ◽  
Author(s):  
Jialei Zhan ◽  
Yaowen Hu ◽  
Guoxiong Zhou ◽  
Yanfeng Wang ◽  
Weiwei Cai ◽  
...  
Keyword(s):  
Forest Fire ◽  
High Accuracy ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Smoke Detection ◽  
High Transparency ◽  
Dilated Convolution ◽  
Fire Smoke ◽  
Feature Pyramid ◽  
Global Optimal

Abstract The occurrence of forest fires can lead to ecological damage, property loss, and human casualties. Current forest fire smoke detection methods do not sufficiently consider the characteristics of smoke with high transparency and no clear edges and have low detection accuracy, which cannot meet the needs of complex aerial forest fire smoke detection tasks. In this paper, we propose Dual-ResNet50-vd with SoftPool based on a recursive feature pyramid with deconvolution and dilated convolution and global optimal nonmaximum suppression (DRGNet) for high-accuracy detection of forest fire smoke. First, the Dual-ResNet50-vd module is proposed to enhance the extraction of smoke features with high transparency and no clear edges, and SoftPool is used to retain more feature information of smoke. Then, a recursive feature pyramid with deconvolution and dilated convolution (RDDFPN) is proposed to fuse shallow visual features and deep semantic information in the channel dimension to improve the accuracy of long-range aerial smoke detection. Finally, global optimal nonmaximum suppression (GO-NMS) sets the objective function to globally optimize the selection of anchor frames to adapt to the aerial photography of multiple smoke locations in forest fire scenes. The experimental results show that the DRGNet parametric number on the UAV-IoT platform is as low as 53.48 M, mAP reaches 79.03%, mAP50 reaches 90.26%, mAP75 reaches 82.35%, FPS reaches 122.5, and GFLOPs reaches 55.78. Compared with other mainstream methods, it has the advantages of real-time detection and high accuracy.

scholarly journals An Effective Method for Forest Fire Smoke Detection

2019 ◽  
Vol 1187 (5) ◽  
pp. 052045 ◽  
Author(s):  
Luxing Qin ◽  
Xuehui Wu ◽  
Yichao Cao ◽  
Xiaobo Lu
Keyword(s):  
Forest Fire ◽  
Smoke Detection ◽  
Fire Smoke

Feasibility of forest-fire smoke detection using lidar

2003 ◽  
Vol 12 (2) ◽  
pp. 159 ◽  
Author(s):  
Andrei B. Utkin ◽  
Armando Fernandes ◽  
Fernando Simões ◽  
Alexander Lavrov ◽  
Rui Vilar
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
High Repetition Rate ◽  
Smoke Detection ◽  
Smoke Plume ◽  
Smoke Plumes ◽  
Lidar Measurements ◽  
Fire Smoke ◽  
532 Nm

The feasibility and fundamentals of forest fire detection by smoke sensing with single-wavelength lidar are discussed with reference to results of 532-nm lidar measurements of smoke plumes from experimental forest fires in Portugal within the scope of the Gestosa 2001 project. The investigations included tracing smoke-plume evolution, estimating forest-fire alarm promptness, and smoke-plume location by azimuth rastering of the lidar optical axis. The possibility of locating a smoke plume whose source is out of line of sight and detection under extremely unfavourable visibility conditions was also demonstrated. The eye hazard problem is addressed and three possibilities of providing eye-safety conditions without loss of lidar sensitivity (namely, using a low energy-per-pulse and high repetition-rate laser, an expanded laser beam, or eye-safe radiation) are discussed.

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