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.
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