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JAIT 2024 Vol.15(4): 511-518
doi: 10.12720/jait.15.4.511-518

Comparison and Analysis of Three MobileNet-Based Models for Wildfire Detection

Shiyan Du 1,*, Jiacheng Li 2, and Masato Noto 3
1. Field of Electrical, Electronics and Information Engineering, Graduate School of Engineering, Kanagawa University, Yokohama, Japan
2. Department of Applied Systems and Mathematics, Kanagawa University, Yokohama, Japan
Email: r202370157bj@jindai.jp (S.D.); lijiacheng@kanagawa-u.ac.jp (J.L.); noto@kanagawa-u.ac.jp (M.N.)
*Corresponding author

Manuscript November 20, 2023; revised December 13, 2023; accepted January 17, 2024; published April 16, 2024.

Abstract—The dynamic equilibrium of ecosystems can be maintained through controlled burning, but excessive wildfires can lead to severe consequences. Therefore, the use of Internet of Things (IoT) devices equipped with deep image processing models for wildfire detection has recently become a trend. Conventional deep image processing models suffer from accuracy issues and large model sizes, limiting their applicability on small IoT devices. To address this challenge, we utilized lightweight deep image processing models such as the MobileNet series to train a wildfire database. Furthermore, we evaluated three different versions of MobileNet (V2, V3 Large, and V3 Small) using a cross-entropy loss function to compare their accuracy and training times. Through data analysis, recommendations for deploying MobileNet models on IoT devices are provided. The results indicate that the ranking of MobileNet’s accuracy from highest to lowest is V2, V3 Large, and V3 Small; the ranking of loss values from lowest to highest is V2, V3 Large, and V3 Small; and the ranking of training times from fastest to slowest is V3 Large, V2, and V3 Small.
Keywords—wildfire detection, MobileNet model, deep learning, comparison and analysis

Cite: Shiyan Du, Jiacheng Li, and Masato Noto, "Comparison and Analysis of Three MobileNet-Based Models for Wildfire Detection," Journal of Advances in Information Technology, Vol. 15, No. 4, pp. 511-518, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.