画像処理を用いた森林火災予測
Yingdan Li1,2, Junting Chen1, Yaxuan Zeng1
1School of Electronic Information Engineering, Guiyang University, Guiyang, China.
PloS one
|January 20, 2026
まとめ
早期の森林火災検出は安全のために不可欠です。新しいYOLOv5-PSGモデルは、リアルタイムの火災認識精度を大幅に向上させ、山火事予防と環境保護を強化します。
科学分野:
- 環境科学; 計算機科学; 人工知能
背景:
- 森林火災は、公共の安全と生態系に実質的なリスクをもたらします。; 小規模な火災が大規模な災害にエスカレートするのを防ぐためには、早期検出が不可欠です。; 従来の火災予測方法は、効果的な介入に必要な精度とリアルタイム能力を欠いています。
研究 の 目的:
- 森林火災予測システムの精度とリアルタイム検出能力を向上させること。; より効果的な早期警戒と山火事予測のために、改良されたYOLOv5-PSGモデルを導入すること。
主な方法:
- 本研究では、YOLOv5-PSGと名付けられたYOLOv5モデルの改良版を提案します。; モデルは300回の厳密なトレーニングと学習を受けました。
主要な成果:
- YOLOv5-PSGモデルは、平均認識精度率93.1% (mAP) を達成しました。; モデルは、トレーニング後、約0.802の精度率と約0.965の信頼度を示しました。; これらの結果は、従来のメソッドに対する大幅な改善を示しています。
結論:
- 改良されたYOLOv5-PSGモデルは、森林火災に対してより包括的で効果的な早期警戒と予測を提供します。; この進歩は、山火事の影響の軽減、人命と環境の保護に貢献します。
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