Every year, forest and wildland fires affect more than 350 million hectares worldwide resulting in important environmental, economic, and social losses. To efficiently fight against this major risk, specific actions are deployed. The efficiency of these actions is tightly linked to the knowledge of the phenomena and in improving the tools for detecting, predicting, and understanding fire propagation. An important step for vision-based fire analysis, is the detection of fire pixels. In this work, we propose Deep-Fire a deep convolutional neural network for fire pixels detection and fire segmentation. The proposed technique is tested on a database of wildland fires. The obtained results, show that the proposed architecture gives a very high performance for the segmentation of wildland and forest fire areas in outdoor non-structured scenarios.
Moulay A. Akhloufi, Roger Booto Tokime, and Hassan Elassady, "Wildland fires detection and segmentation using deep learning," Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 106490B (Presented at SPIE Defense + Security: April 18, 2018; Published: 27 April 2018); https://doi.org/10.1117/12.2304936.
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