基于YOLOv8的复杂无人机森林环境中的多层次野火和烟雾检测
Wenyu Zhu1,2, Shanwei Niu3, Jixiang Yue4
1School of Mechanical and Electrical Engineering, China University of Petroleum Huadong, Qingdao, 266580, China.
Scientific reports
|January 18, 2025
概括
本研究介绍了一种人工智能模型,用于使用无人机检测森林火灾,提高准确性和效率. 增强的YOLOv8模型为森林火灾提供了更好的早期预警和应对能力.
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:
- 气候变化加剧了森林火灾,需要先进的监测.
- 传统的方法,如手动检查和卫星遥感,在实时森林火灾检测方面存在局限性.
- 无人机与人工智能 (AI) 结合,正在成为有效火灾监测的主流解决方案.
研究的目的:
- 开发一个改进的人工智能模型,用于使用无人机增强森林火灾检测.
- 解决现有模型在准确性,效率和对环境变化的强度方面的局限性.
- 改善森林火灾的预警系统和应急响应.
主要方法:
- 开发了一种改进的基于YOLOv8的深度学习模型.
- 整合了局部卷积和EMA模块,以增强功能交互并降低复杂性.
- 在脊柱中引入了AgentAttention (将Softmax和线性注意力结合起来),以实现强大的特征提取.
- 设计了BiFormer模块,用于全球和本地特征的自适应融合,以改善多尺度和多角度检测.
主要成果:
- 改进后的模型实现了93.57%的精度和88.51%的回忆,超过了原始模型的性能.
- 显著提高了效率,FPS增加了14.3%,GFLOP减少了25%,Params减少了19.7%.
- 该模型在各种条件下在检测火焰和烟雾方面表现出更高的准确性和稳定性.
结论:
- 拟议的AI模型在基于无人机的森林火灾检测方面取得了重大进展.
- 这些改进带来了更高的准确性,更高的效率和更好的稳定性,这对于实时监控至关重要.
- 这项研究为森林火灾预警,应急响应和资源保护提供了强有力的技术支持.
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