Affiliation:
1. Perception, Robotics and Intelligent Machines (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1A 3E9, Canada
Abstract
Fire accidents cause alarming damage. They result in the loss of human lives, damage to property, and significant financial losses. Early fire ignition detection systems, particularly smoke detection systems, play a crucial role in enabling effective firefighting efforts. In this paper, a novel DL (Deep Learning) method, namely BoucaNet, is introduced for recognizing smoke on satellite images while addressing the associated challenging limitations. BoucaNet combines the strengths of the deep CNN EfficientNet v2 and the vision transformer EfficientFormer v2 for identifying smoke, cloud, haze, dust, land, and seaside classes. Extensive results demonstrate that BoucaNet achieved high performance, with an accuracy of 93.67%, an F1-score of 93.64%, and an inference time of 0.16 seconds compared with baseline methods. BoucaNet also showed a robust ability to overcome challenges, including complex backgrounds; detecting small smoke zones; handling varying smoke features such as size, shape, and color; and handling visual similarities between smoke, clouds, dust, and haze.
Funder
The Natural Sciences and Engineering Research Council of Canada
Subject
Earth and Planetary Sciences (miscellaneous),Safety Research,Environmental Science (miscellaneous),Safety, Risk, Reliability and Quality,Building and Construction,Forestry
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