最佳的功能是辅助多注意力融合,用于在恶劣条件下强大的火灾识别
Inam Ullah1, Nada Alzaben2, Yousef Ibrahim Daradkeh3
1Department of Computer Engineering, Gachon University, Seongnam, 13120, South Korea.
Scientific reports
|July 4, 2025
概括
本研究介绍了注意力增强火灾识别网络 (AEFRN),以改进视觉火灾检测. AEFRN显著减少了虚假警报,并提高了在困难条件下的识别,在基准数据集上实现了高精度.
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:
- 深度神经网络在视觉火灾检测方面表现有前途,但在具有挑战性的环境中存在高错误报警率和不良性能.
- 现有的方法通常利用浅层网络,限制其捕获复杂火灾特征和上下文的能力.
研究的目的:
- 开发一种新的深度学习框架,即注意力增强火灾识别网络 (AEFRN),以克服当前视觉火灾检测系统的局限性.
- 为了提高准确性和减少火灾检测中的错误报警,同时保持实际应用的计算效率.
主要方法:
- 引入了卷积自我注意 (CSA),将自我注意与卷积整合起来,以增强低级特征处理.
- 开发了递归门自我注意力 (RASA),以高效地捕获多层次的上下文信息.
- 增强了卷积块注意模块 (CBAM) 以实现强大的特征歧视.
主要成果:
- 在FD数据集上以99.11%的准确度实现了最先进的 (SOTA) 性能,在BoWFire数据集上以97.98%的准确度实现了最先进的 (SOTA) 性能.
- 与12种SOTA方法相比,在具有挑战性的火灾检测场景中表现出优越的性能.
- 使用Grad-CAM验证了模型的解释性,显示专注于与火灾相关的区域.
结论:
- AEFRN有效地解决了视觉火灾检测方面的局限性,提供高精度和可靠性.
- 提出的注意力机制增强了特征处理和上下文理解,以实现强大的火灾识别.
- AEFRN为先进的火灾检测系统提供了计算效率高且实用的解决方案.
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