FM-Net:一种用于检测烟雾和火焰的新方法
Jingwu Wang1,2, Yuan Yao1, Yinuo Huo2
1School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230601, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究引入了一种改进的火灾检测网络,可以显著减少错误报警,并在复杂环境中提高准确性. 新的算法提供实时检测功能,以提高安全性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 消防安全工程 消防安全工程
背景情况:
- 现有的烟雾和火灾检测算法在复杂场景中扎着高错误报警率和低干扰阻力.
- 传统的卷积运算可以在下方采样过程中导致边缘特征损失,阻碍准确的检测.
研究的目的:
- 开发一个先进的目标检测网络,以提高准确性和稳定性来检测烟雾和火灾.
- 解决复杂环境和实时应用中现有算法的局限性.
主要方法:
- 提出了一种新的目标检测网络,利用改进的特征金字塔结构.
- 引入了上下文引导的卷积块来融合目标和环境信息,减轻边缘特征的损失.
- 设计的多核起始块用于多尺度的特征提取和火焰和烟雾的协作表征.
- 整合了曼哈顿注意力机制单元,用于增强的时空相关性和像素级依赖模型.
主要成果:
- 与现有的轻量级模型相比,拟议的算法在检测准确度方面显示出显著的优势.
- 在自建数据集上的实验结果验证了算法的有效性.
- 该系统满足了对实时烟雾和火灾检测的需求.
结论:
- 改进的特征金字塔网络有效地提高了烟雾和火灾检测准确性和抗干扰性.
- 新型网络组件有助于更好的特征融合,多尺度分析和时空相关性.
- 这个算法为可靠的实时火灾检测系统提供了一个有前途的解决方案.
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