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Updated: Feb 28, 2026

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一个轻量级的火灾检测框架,用于边缘视觉传感器,使用小样本域调整.
Jie Hu1, Ruitong Yao1, Qingyuan Yang2
1Shanxi Agricultural University, Jinzhong 030800, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了一种使用多功能融合和自适应支持矢量机器 (A-SVM) 用于基于视觉传感器网络的新型火灾检测系统. 该方法实现了强大的跨场景火灾检测,在具有挑战性的条件下提高了准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 传感器网络 传感器网络
背景情况:
- 基于视觉的传感器网络面临着由于不同的环境条件而导致准确火灾检测的挑战.
- 为了训练可靠的模型,很难为各种场景 (日夜,干扰) 获取标记数据.
研究的目的:
- 为基于视觉的传感器网络开发一种新的火灾检测框架.
- 通过域调整,在不同场景中提高火灾检测准确性和稳定性.
主要方法:
- 通过融合HSI颜色统计,局部二进制模式 (LBP) 纹理和波形转换形状特征来构建一个高维特征向量.
- 使用具有小样本域适应机制的自适应支持矢量机器 (A-SVM),以微调具有有限目标域数据的模型.
- 利用规范化约束来有效调整模型参数.
主要成果:
- 与基线方法相比,拟议的多功能融合和A-SVM方法显著提高了F1得分19% (白天) 和30% (夜间).
- 在跨域情景中,在传统的颜色值和未经调整的SVM分类器上表现出优异的性能.
- 实现了低成本,高精度和强大的火灾检测.
结论:
- 开发的框架为基于视觉的传感器网络的跨场景火灾检测提供了有效的解决方案.
- 该方法非常适合在资源受限的边缘计算节点上部署.
- 域名适应对于在各种环境条件下提高火灾检测系统性能至关重要.
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