In this paper, we design a hybrid kitchen safety guarding framework using embedded devices and onboard sensors to detect abnormal events and block gas sources in time through the Internet of Things (IoT). According to the relevant literature we studied, this is the first framework for kitchen safety guarding that provides the following features: (1) the deep learning based model integrating densely connected convolutional networks with neural architecture search networks is developed to accurately recognize abnormal stove fire, (2) the acceleration correction method is designed to correct the sensed accelerometer values for estimating the actual earthquake level, and (3) the proper gas leakage threshold is defined to precisely detect the gas leakage for automatic gas blocking, and remote surveillance and control are provided to monitor the kitchen environment and control the gas source anytime and anywhere. In particular, an Android-based prototype consisting of the IoT device, diverse sensors, dedicated server, and smartphones is implemented to verify the feasibility and superiority of our framework. Experimental results show that our framework outperforms existing methods and can precisely recognize stove fire intensity and detect earthquake levels for kitchen safety guarding.

中文翻译：

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基于物联网的灶火识别混合厨房安全守护


在本文中，我们设计了一种混合厨房安全防护框架，利用嵌入式设备和板载传感器来通过物联网（IoT）检测异常事件并及时阻止气源。根据我们研究的相关文献，这是第一个厨房安全防护框架，具有以下特点：（1）开发了将密集连接的卷积网络与神经架构搜索网络相结合的基于深度学习的模型，以准确识别异常炉火， (2)设计加速度校正方法，对感测的加速度计值进行校正，以估计实际地震级别；(3)定义合适的气体泄漏阈值，精确检测气体泄漏，实现自动气体封锁，并进行远程监视和控制。随时随地监控厨房环境，控制气源。特别是，实现了一个由物联网设备、各种传感器、专用服务器和智能手机组成的基于Android的原型，以验证我们框架的可行性和优越性。实验结果表明，我们的框架优于现有方法，可以精确识别炉火强度并检测地震级别以实现厨房安全防护。