使用图像处理进行森林火灾预测
Yingdan Li1,2, Junting Chen1, Yaxuan Zeng1
1School of Electronic Information Engineering, Guiyang University, Guiyang, China.
PloS one
|January 20, 2026
概括
早期森林火灾检测对于安全至关重要. 一个新的YOLOv5-PSG模型显著提高了实时火灾识别准确度,增强了防火和环境保护.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 森林火灾对公众安全和生态系统构成重大风险.
- 早期检测至关重要,以防止小火灾升级为重大灾难.
- 传统的火灾预测方法缺乏有效干预所需的准确性和实时能力.
研究的目的:
- 提高森林火灾预测系统的准确性和实时检测能力.
- 引入一个改进的YOLOv5-PSG模型,以更有效地预警和预测野火.
主要方法:
- 该研究提出了YOLOv5模型的改进版本,称为YOLOv5-PSG.
- 该模型经历了300轮严格的训练和学习.
主要成果:
- 该YOLOv5-PSG模型实现了93.1% (mAP) 的平均识别准确率.
- 该模型在训练后显示了大约0.802的准确率和大约0.965的信心水平.
- 这些结果表明,与传统方法相比,有了显著的改进.
结论:
- 增强的YOLOv5-PSG模型为森林火灾提供了更全面,更有效的早期预警和预测.
- 这一进步有助于更好地减轻野火的影响,保护人类生命和环境.
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