深度学习对象检测方法的研究和应用,用于森林火灾,烟雾识别和烟雾识别
Luhao He1,2,3, Yongzhang Zhou4,5,6, Lei Liu1,2,3
1Center for Earth Environment and Earth Resources, Sun Yat-sen University, Zhuhai, 519000, Guangdong, China.
Scientific reports
|May 10, 2025
概括
这项研究引入了YOLOv11x,用于有效地检测森林火灾的烟雾,实现早期预警系统的高精度和可靠性. 深度学习模型显示了提高野火监测能力的巨大潜力.
科学领域:
- 计算机科学 计算机科学
- 环境科学 环境科学
- 人工智能的人工智能
背景情况:
- 森林火灾带来了重大的生态和经济威胁,气候变化加剧了这种威胁.
- 有效的监测和预警系统对于减轻野火损害至关重要.
- 深度学习为自动火灾检测提供了有希望的解决方案.
研究的目的:
- 评估YOLOv11x算法的有效性,用于基于深度学习的森林火灾烟雾识别.
- 开发一个高效的火灾检测模型,以提高早期检测能力.
- 通过使用各种数据集,提高火灾检测模型的通用性.
主要方法:
- 使用YOLOv11x算法在森林火灾场景中检测物体.
- 在两个公共数据集上训练模型:WD (野火数据集) 和FFS (森林火灾烟雾).
- 使用精度,回忆和平均平均精度 (mAP50,mAP50-95) 等指标评估模型性能.
主要成果:
- YOLOv11x表现出强大的性能,精度为0.949,回忆率为0.850,mAP50为0.901,mAP50-95为0.786.
- 与火焰检测 (mAP@0.5 = 0.841) 相比,该模型在烟雾检测 (mAP@0.5 = 0.962) 中表现出更好的性能.
- 86.89%的测试样本获得了超过0.85的置信度得分,这表明可靠性很高.
结论:
- 对于森林火灾的烟雾识别,YOLOv11x算法非常有效.
- 该模型为早期火灾预警系统提供了强大的技术支持.
- 这些发现为设计用于防范野火的智能监控系统提供了宝贵的见解.
相关概念视频
01:19Gas Chromatography: Types of Detectors-II
288
In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...
288
01:02Flame Photometry: Overview
370
Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
370