Mobile device

Convolutional neural network

Internet of Things

Engineering

Edge device

Artificial intelligence

Deep learning

Fire detection

Computational intelligence

Geography

Operating system

Enhanced Data Rates for GSM Evolution

Computer science

Cloud computing

Geodesy

Benchmark (surveying)

Real-time computing

Embedded system

Architectural engineering

Edge computing

IEEE Transactions on Industrial Informatics

Tactile Internet can combine multiple technologies by enabling intelligence via mobile edge computing and data transmission over a 5G network. Recently, several convolutional neural networks (CNN) based methods via edge intelligence are utilized for fire detection in certain environment with reasonable accuracy and running time. However, these methods fail to detect fire in uncertain Internet of Things (IoT) environment having smoke, fog, and snow. Furthermore, achieving good accuracy with reduced running time and model size is challenging for resource constrained devices. Therefore, in this paper, we propose an efficient CNN based system for fire detection in videos captured in uncertain surveillance scenarios. Our approach uses light-weight deep neural networks with no dense fully connected layers, making it computationally inexpensive. Experiments are conducted on benchmark fire datasets and the results reveal the better performance of our approach compared to state-of-the-art. Considering the accuracy, false alarms, size, and running time of our system, we believe that it is a suitable candidate for fire detection in uncertain IoT environment for mobile and embedded vision applications during surveillance.

Efficient Fire Detection for Uncertain Surveillance Environment