A lightweight fire detection for edge computing based on Mobilenet

Hao Yuan; Hao Yang; Ruiqi Li; Peng Xu; Shuiting Du; Rui Dong

doi:10.1117/12.2680768

23 May 2023 A lightweight fire detection for edge computing based on Mobilenet

Hao Yuan, Hao Yang, Ruiqi Li, Peng Xu, Shuiting Du, Rui Dong

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126453G (2023) https://doi.org/10.1117/12.2680768
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

With the rapid development of neural network, significant breakthroughs have been made in many aspects of object detection which has been widely used in many fields such as intelligent transportation and industrial detection. The State Grid is also focusing on object detection and related technologies to advance the intelligence of China's power grid in operations and maintenance and other fields. Therefore, in this paper we propose a fire detection oriented in server room. Since edge computing ensures responsiveness and reduces server load, object detection is better suited for deployment under edge computing. However, embedded devices in edge computing often have significant limitations in terms of storage and energy consumption. Traditional object detection algorithms are implemented by neural networks, which require large computational and storage resources, which conflict with edge computing. Therefore, we implement lightweight fire detection algorithms for edge computing via MobileNet to reduce parameter size and increase speed, and enhance multi-scale detection capabilities with the YOLOF algorithm.

Citation Download Citation

Hao Yuan, Hao Yang, Ruiqi Li, Peng Xu, Shuiting Du, and Rui Dong "A lightweight fire detection for edge computing based on Mobilenet", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126453G (23 May 2023); https://doi.org/10.1117/12.2680768

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KEYWORDS

Fire

Convolution

Detection and tracking algorithms

Edge detection

Education and training

Neural networks

Feature fusion

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