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Sensors and Materials, Volume 37, Number 8(3) (2025)
Copyright(C) MYU K.K.
pp. 3621-3631
S&M4141 Research paper of Special Issue
https://doi.org/10.18494/SAM5716
Published: August 21, 2025

High-precision Wildfire Detection Algorithm Using an Enhanced You Only Look Once Model in a Jetson Xavier Environment [PDF]

Tae-Hwan Kim, Eun-Su Seo, and Se-Hyu Choi

(Received April 29, 2025; Accepted August 8, 2025)

Keywords: deep neural networks, artificial intelligence, YOLO, Xavier, wildfire smoke

The You Only Look Once (YOLO) algorithm is employed to develop a system for detecting wildfire smoke and providing early warnings, with a deep neural network (DNN) as its underlying architecture. To achieve accurate real-time smoke detection, the system was optimized through experiments conducted under diverse conditions. It was then implemented in an embedded computing environment to enhance the efficiency of wildfire detection. The findings demonstrate the effectiveness of this DNN-based smoke detection system in real-world environments.

Corresponding author: Se-Hyu Choi


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Tae-Hwan Kim, Eun-Su Seo, and Se-Hyu Choi, High-precision Wildfire Detection Algorithm Using an Enhanced You Only Look Once Model in a Jetson Xavier Environment, Sens. Mater., Vol. 37, No. 8, 2025, p. 3621-3631.

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