Wildland fires detection and segmentation using deep learning

Moulay A. Akhloufi; Roger Booto Tokime; Hassan Elassady

doi:10.1117/12.2304936

27 April 2018 Wildland fires detection and segmentation using deep learning

Moulay A. Akhloufi, Roger Booto Tokime, Hassan Elassady

Proceedings Volume 10649, Pattern Recognition and Tracking XXIX; 106490B (2018) https://doi.org/10.1117/12.2304936
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

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.

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Citation Download Citation

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 (27 April 2018); https://doi.org/10.1117/12.2304936

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