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• W2834632380 abstract "In this work we investigate the automatic detection of fire pixel regions in video (or still) imagery within real-time bounds without reliance on temporal scene information. As an extension to prior work in the field, we consider the performance of experimentally defined, reduced complexity deep convolutional neural network architectures for this task. Contrary to contemporary trends in the field, our work illustrates maximal accuracy of 0.93 for whole image binary fire detection, with 0.89 accuracy within our superpixel localization framework can be achieved, via a network architecture of signficantly reduced complexity. These reduced architectures additionally offer a 3-4 fold increase in computational performance offering up to 17 fps processing on contemporary hardware independent of temporal information. We show the relative performance achieved against prior work using benchmark datasets to illustrate maximally robust real-time fire region detection." @default.
• W2834632380 created "2018-07-19" @default.
• W2834632380 creator A5045115593 @default.
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• W2834632380 date "2018-10-01" @default.
• W2834632380 modified "2023-09-30" @default.
• W2834632380 title "Experimentally Defined Convolutional Neural Network Architecture Variants for Non-Temporal Real-Time Fire Detection" @default.
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• W2834632380 doi "https://doi.org/10.1109/icip.2018.8451657" @default.
• W2834632380 hasPublicationYear "2018" @default.
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