一个基于改进的YOLOv8森林火灾识别和监测模型
Yunchang Zheng1, Pengzhu Guo1, Xiaokai Tian1
1College of Electrical Engineering, Hebei University of Architecture, Zhangjiakou, 075000, China.
Scientific reports
|October 23, 2025
概括
本研究介绍了一种优化的YOLOv8模型,用于增强森林火灾检测. 新型号提高了早期火灾检测的准确性,并减少了复杂环境中的错误报警.
科学领域:
- 环境科学 环境科学
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
背景情况:
- 精确的森林火灾监测对于减轻生态和经济损害至关重要.
- 目前基于无人机的视觉监控正在努力检测小型火灾/烟雾,并将其与背景干扰区分开来.
研究的目的:
- 开发一个强大而高效的深度学习模型,以改进森林火灾检测.
- 解决检测小型火灾指示器的局限性,减少复杂自然环境中的错误阳性.
主要方法:
- 基于YOLOv8架构开发了一个优化的深度学习模型.
- 该模型具有创新的多模块协作设计,用于增强检测能力.
主要成果:
- 与基准模型相比,拟议模型的平均精度 (mAP) 提高了4.7%.
- 错误检测率显著下降,特别是在具有挑战性的环境条件下.
结论:
- 优化的YOLOv8模型有效地提高了森林火灾检测的准确性和稳定性.
- 这一进步提高了实时森林火灾监测系统的可靠性和实用性.
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