Wildfire Identification Based on an Improved MobileNetV3-Small Model DOI Open Access
Guanggang Shi, Yina Wang, Zhenfa Yang

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(11), P. 1975 - 1975

Published: Nov. 8, 2024

In this paper, an improved MobileNetV3-Small algorithm model is proposed for the problem of poor real-time wildfire identification based on convolutional neural networks (CNNs). Firstly, a dataset constructed and subsequently expanded through image enhancement techniques. Secondly, efficient channel attention mechanism (ECA) utilised instead Squeeze-and-Excitation (SE) module within to enhance model’s speed. Lastly, support vector machine (SVM) employed replace classification layer model, with principal component analysis (PCA) applied before SVM reduce dimensionality features, thereby enhancing SVM’s efficiency. The experimental results demonstrate that achieves accuracy 98.75% average frame rate 93. Compared initial mean has been elevated by 7.23. designed in paper improves speed while maintaining accuracy, advancing development application CNNs field monitoring.

Language: Английский

Wildfire Identification Based on an Improved MobileNetV3-Small Model DOI Open Access
Guanggang Shi, Yina Wang, Zhenfa Yang

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(11), P. 1975 - 1975

Published: Nov. 8, 2024

In this paper, an improved MobileNetV3-Small algorithm model is proposed for the problem of poor real-time wildfire identification based on convolutional neural networks (CNNs). Firstly, a dataset constructed and subsequently expanded through image enhancement techniques. Secondly, efficient channel attention mechanism (ECA) utilised instead Squeeze-and-Excitation (SE) module within to enhance model’s speed. Lastly, support vector machine (SVM) employed replace classification layer model, with principal component analysis (PCA) applied before SVM reduce dimensionality features, thereby enhancing SVM’s efficiency. The experimental results demonstrate that achieves accuracy 98.75% average frame rate 93. Compared initial mean has been elevated by 7.23. designed in paper improves speed while maintaining accuracy, advancing development application CNNs field monitoring.

Language: Английский

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