• Fire Science and Engineering
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• v.24 no.4
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• pp.1-11
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• 2010

This paper describes a design and implementation of an analog detector and a fire alarm system with recent technology on information and communication. A hierarchical architecture design from the detector to the main system enables to accommodate medium to large size buildings located nearby or far-away. And a software design from communication protocol to application program handles large amount of events efficiently to show information on a large LCD. A PC-based alarm system provides higher speed and larger capacity in a large LCD screen compared with foreign microprocessor-based small screen systems. Thus, very large buildings with several thousands of analog detectors can be easily covered in a single system. When an alarm occurs, a staff alarm scenario specially attempted only in the system is considered to play a major role to distinguish a real fire from unwanted alarms.

• Journal of IKEEE
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• v.23 no.3
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• pp.777-784
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• 2019

A pre-fire awareness and automatic notification system are required because it is possible to minimize the damage if the fire situation is precisely detected after a fire occurs in a place where people are unusual or in a mountainous area. In this study, we developed a RaspberryPi-based fire recognition system using Faster-recurrent convolutional neural network (F-RCNN) and single shot multibox detector (SSD) and demonstrated a fire alarm system that works with power line communication. Image recognition was performed with a pie camera of RaspberryPi, and the detected fire image was transmitted to a monitoring PC through an inductive power line communication network. The frame rate per second (fps) for each learning model was 0.05 fps for Faster-RCNN and 1.4 fps for SSD. SSD was 28 times faster than F-RCNN.