基于边缘的自主火灾和烟雾检测使用MobileNetV2
Dilshod Sharobiddinov1, Hafeez Ur Rehman Siddiqui2, Adil Ali Saleem2
1Department of Computer Science, Ulster University, London Branch Campus, St James' House, 10 Rosebery Avenue, Holborn, London EC1R 4TF, UK.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
本研究介绍了一种高效的基于边缘的森林火灾和烟雾检测系统,使用轻量级的深度学习模型进行检测. 自主系统近乎实时实现高精度,可在偏远地区早期缓解野火.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 森林火灾对生态,人类和气候构成重大威胁,需要先进的检测方法.
- 现有的检测系统 (传感器,卫星,集中分析) 面临着延迟,错误报警和限制部署等局限性.
- 当前的深度学习模型虽然准确,但对于实时边缘应用程序来说往往太耗费资源.
研究的目的:
- 开发一个自主,基于边缘的森林火灾和烟雾检测系统.
- 为资源有限的边缘设备优化轻量级的深度学习模型.
- 为了实现实时,局部的野火检测和早期缓解.
主要方法:
- 使用轻量级的MobileNetV2卷积神经网络进行图像分析.
- 在火,烟和非火图像的平衡数据集上训练模型.
- 在Raspberry Pi 5边缘设备上部署和优化系统以实现自主操作.
主要成果:
- 在火灾和烟雾检测方面取得了97.98%的测试准确度.
- 证明了近实时推断,每平均延迟为0.77秒 (1.3 FPS).
- 在本地生成预测,确保安全性和稳定性,而无需依赖云.
结论:
- 拟议的基于边缘的系统为远程野火监测提供了具有成本效益和可扩展性的解决方案.
- 结合高精度,速度和自主边缘处理,可及时检测火灾.
- 增强环境监测和早期野火减轻能力.
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