多级特征融合网络用于远程传感图像中的烟雾识别
Yupeng Wang1, Yongli Wang1, Zaki Ahmad Khan2
1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
概括
一个新的多级特征融合网络 (MFFNet) 在遥感图像中有效检测森林火灾烟雾. 这种先进的深度学习模型显著减少了错误警报,改善了早期火灾检测系统.
科学领域:
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
- 林业和消防管理
背景情况:
- 远程传感中精确的烟雾检测对于森林火灾监测至关重要,特别是在物联网 (IoT) 系统中.
- 现有的方法难以应对复杂的场景,包括可变的烟雾外观,杂乱的背景和类似烟雾的现象 (云,雾),导致错过检测和错误报警.
研究的目的:
- 开发一种新的深度学习框架,即多级特征融合网络 (MFFNet),用于在遥感图像中进行强大而准确的烟雾检测.
- 通过增强特征提取和融合来克服当前烟雾检测技术的局限性,以改善对背景噪声和类似现象的歧视.
主要方法:
- 使用预训练的ConvNeXt模型从遥感图像中进行多尺度的特征提取.
- 整合了注意力功能增强模块,以改进多尺度功能,强调歧视性烟雾属性.
- 采用双线特征融合模块来实现特征集成,减少背景干扰和对比特征学习以提高稳定性.
主要成果:
- 在基准的USTC_SmokeRS数据集上,MFFNet实现了98.87%的高精度.
- 在扩展的E_SmokeRS数据集上显示了94.54%的检测率,其低误报率为3.30%.
- 在复杂的遥感图像中超越现有的烟雾识别方法.
结论:
- 多国财政基金网在森林火灾监测的烟雾检测技术方面取得了重大进展.
- 拟议框架的多层次特征融合和对比学习方法有效地解决了复杂环境和视觉相似性带来的挑战.
- 该模型的高精度和低误报率强调了其在早期野火检测系统中实际实施的潜力.
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