火灾和烟雾检测的频道注意力:增强,颜色空间和对抗性攻击的影响
Usama Ejaz1, Muhammad Ali Hamza1, Hyun-Chul Kim1
1Department of Software, Sangmyung University, Cheonan 31066, Republic of Korea.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
这项研究引入了一种用于野火检测的新的道智能注意力架构,达到95%的准确性. 该模型增强了火焰特征识别,提高了复杂环境中的可靠性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 环境监测 环境监测
背景情况:
- 野火检测系统在复杂的背景和不同的环境条件下面临挑战.
- 可靠的火灾检测对于可持续和有效的野火管理至关重要.
研究的目的:
- 开发一个先进的火灾检测系统,使用基于道智能的注意力架构.
- 在各种条件下评估模型的性能,稳定性和漏洞.
主要方法:
- 引入了一个通道智能的注意体系结构,以改善火焰特征的焦点.
- 进行了除研究,并评估了RGB,灰度,HSV和YCbCr颜色空间的性能.
- 使用快梯度信号方法 (FGSM) 扰动和局部可解释模型-不可知解释 (LIME) 评估模型漏洞.
主要成果:
- 达到95%的准确性,注意力机制比基线模型提高了3-5%.
- 数据增强在各种环境条件下提高了检测性能.
- 由于FGSM的干扰,准确度降至41%,这表明模型存在漏洞.
结论:
- 道智能关注架构显著提高了火灾检测准确性和可靠性.
- 模型的稳定性受环境条件和对抗性攻击的影响.
- LIME可视化为改进的火灾检测应用提供了对模型决策的洞察力.
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