Experimental Fire Detection and Alarm System Based on the Integration of IoT and Image Processing
Fires are considered a risk that causes human death and property loss. To reduce fire damage, fires must be detected and warned promptly. This paper aims to design and experimentally implement a fully-function fire detection and warning system. The developed system is based on a Wireless Sensor Network (WSN) that includes various nodes distributed across the city. Each node has two main blocks which are the image processing and communication blocks. The image processing block applies the EfficientN et B0 model to detect the fire and smoke, whereas the communication block uses the Module 4G to contact the house owner over a mobile phone network and LoRa gateway to send the signal warning to the fire center over the a LoRaWAN. The Module 4G is responsible for sending captured images of suspected fire if any to the house owner's device for getting his/her acknowledgement. Then an urgent alarm message consisting of fire position and risk level are sent to the fire center if the fire is acknowledged by the house owner, otherwise, the alarm is ceased. Besides, the proposed system is installed the gas sensor, smoke sensor, water spray system and light-sound alarm to augment the reliability of fire detection. The proposed system is experimentally demonstratedand achieved positive results: Delay time sending from the proposed fire detection system to the user is recorded as 5 seconds and from the user to the fire center is recorded as 10 seconds, within a LoRaWAN coverage of1.5km.
Authors
- Phu Dung Le
- Văn Thành Trần
- Tran The Son (ORCID: https://orcid.org/0000-0002-4903-0417)
- V. V. Tran (ORCID: https://orcid.org/0009-0005-3625-0372)
- Dat Vuong (ORCID: https://orcid.org/0000-0003-1964-7106)
- Huy Dat Tran (ORCID: https://orcid.org/0000-0003-0223-6140)
Publication Details
- Published
- 2024-11-03
- DOI
- https://doi.org/10.1109/icce-asia63397.2024.10773659
- Citations
- 1
- Primary Topic
- Fire Detection and Safety Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.36