2022
DOI: 10.11591/ijece.v12i3.pp3118-3128
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Comparison of two deep learning methods for detecting fire hotspots

Abstract: Every high-rise building must meet construction requirements, i.e. it must have good safety to prevent unexpected events such as fire incident. To avoid the occurrence of a bigger fire, surveillance using closed circuit television (CCTV) videos is necessary. However, it is impossible for security forces to monitor for a full day. One of the methods that can be used to help security forces is deep learning method. In this study, we use two deep learning methods to detect fire hotspots, i.e. you only look once (… Show more

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Cited by 3 publications
(2 citation statements)
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“…We followed the setup in [19] and divided the dataset into two parts for this study: the training set and the test set. The training set, which contains 84 data points (70%), is used to train the model.…”
Section: Research Processmentioning
confidence: 99%
“…We followed the setup in [19] and divided the dataset into two parts for this study: the training set and the test set. The training set, which contains 84 data points (70%), is used to train the model.…”
Section: Research Processmentioning
confidence: 99%
“…In the next stage, we evaluate the model by calculating the value of precision, recall, and accuracy (Acc) in each category using (10), ( 11), ( 12), (13), and ( 14) [36]- [38].…”
Section: Model Evaluationmentioning
confidence: 99%