Fire Identification System in Interior Spaces
DOI:
https://doi.org/10.32870/recibe.v12i1.256Keywords:
Fire detection, convolutional neural networks,, preventionAbstract
Fires in closed spaces are characterized by being detonated when an electrical installation is causing a short circuit or by a source that causes a spark in said spaces, in addition, there is the presence of fuels such as gases and highly flammable solid or liquid materials. This research focuses on presenting a hybrid system for the prevention and detection of fires in closed places, such as a homeroom, corporate buildings, laboratories, among others. The implementation of sensors for the detection of different types of flammable gases has been proposed, as well as the detection of non-flammable gases that are the product of a combustion process, in addition to this, there is a computer vision detection system, the which uses a deep neural network for fire and smoke detection based on the You Only Look Once (YOLO) model. The variables obtained by the device, called DRI3 (Gas and Image Recognition Device), can store the records on a local server, as well as upload the data obtained to the ThingSpeak platform for information backup and analysis. According to the results obtained, the system had a 100% detection capacity of flammable gases and smoke, while the convolutional network used to detect fire in digital images had a detection capacity of 93%, additionally, the emission of alerts by sending alert messages to cell phones was managed correctly.References
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