TY - JOUR
T1 - Automation Tool for Home Fire Safety Check
AU - Tsai, Rong Guei
AU - Tsai, Yi Yun
AU - Tsai, Pei Hsuan
N1 - Funding Information:
This work was supported by the Ministry of Science and Technology and the Industrial Technology Research Institute under Grant MOST 108-2221-E-006-095-MY2. The work of Rong-Guei Tsai was supported in part by the Education Department of the Fujian Province Project, China under Grant JAT190580 and in part by the Science Foundation of the Fujian Province Project, China under Grant 2020J01924.
Publisher Copyright:
© 2017 IEEE.
PY - 2021/12/1
Y1 - 2021/12/1
N2 - Fire simulation tools have been developed for the inspection of environmental safety. However, they have not been extensively exploited for home use. One critical reason for this is that building the environmental model relies highly on manual operations. In this letter, image sensors are adopted to automatically build an environmental model and reduce the labor burden of exploiting fire simulation tools for a fire safety check. To increase the accuracy of an environmental model, our automation extracts the essential features affecting fires from images using recognition models and the proposed approaches. Our automation converts the environmental data into an FDS6 syntax. The experimental results reveal the feasibility of our automation. Compared to a manual operation, our approach not only reduces the labor burden but also the inconsistent environmental model owing to a manual operation as well.
AB - Fire simulation tools have been developed for the inspection of environmental safety. However, they have not been extensively exploited for home use. One critical reason for this is that building the environmental model relies highly on manual operations. In this letter, image sensors are adopted to automatically build an environmental model and reduce the labor burden of exploiting fire simulation tools for a fire safety check. To increase the accuracy of an environmental model, our automation extracts the essential features affecting fires from images using recognition models and the proposed approaches. Our automation converts the environmental data into an FDS6 syntax. The experimental results reveal the feasibility of our automation. Compared to a manual operation, our approach not only reduces the labor burden but also the inconsistent environmental model owing to a manual operation as well.
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U2 - 10.1109/LSENS.2021.3124800
DO - 10.1109/LSENS.2021.3124800
M3 - Article
AN - SCOPUS:85118667300
SN - 2475-1472
VL - 5
JO - IEEE Sensors Letters
JF - IEEE Sensors Letters
IS - 12
ER -