ADFireNet:基于可变形卷积的无烟雾和火灾检测网络.
1School of Computer Science, Northeast Electric Power University, Jilin 132011, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
我们开发了ADFireNet,这是一个无火灾和烟雾检测系统,使用可变形卷积来增强特征提取. 与现有方法相比,该网络实现了更高的准确性和更快的检测速度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 准确有效地检测火灾和烟雾对于公共安全至关重要.
- 现有的物体检测方法经常与火和烟的复杂视觉特征作斗争.
- 无方法提供潜在的优势,但在标签分配方面面临挑战.
研究的目的:
- 提出一个新的无网络,ADFireNet,用于改进烟雾和火灾检测.
- 使用可变形卷曲增强火和烟的特征提取能力.
- 解决无网络中的标签分配问题,在联盟上有伪交叉点.
主要方法:
- 开发了ADFireNet,将ResNet与可变形卷积 (DCN) 集成到骨干中.
- 在部使用特征金字塔网络 (FPN) 进行多层次检测.
- 使用无头,具有伪交叉与联合 (伪IoU) 进行分类和界限框回归.
主要成果:
- 在火灾烟雾数据集上,ADFireNet表现出卓越的准确性和更快的检测速度.
- 废弃性研究证实了DCN和伪IOU对性能的显著贡献.
- 拟议的网络有效地增强了用于火灾和烟雾检测的形状特征提取.
结论:
- ADFireNet为实时和准确的火灾和烟雾检测提供了一个有前途的解决方案.
- 集成DCN和伪IoU有效地克服了当前无检测系统的局限性.
- 拟议的方法显示出在安全关键应用中部署的巨大潜力.
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