발간년도 : [2022]
| 논문정보 |
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| 논문명(한글) |
[Vol.17, No.6] Deep Learning Based Fire And Smoke Detection Systems in Camera |
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| 논문투고자 |
Solbi Son, Sangmin Suh |
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| 논문내용 |
Fires causes a lot of property damage and death. Therefore, early detection of fires is critical to protect property and life. In order to detect the fires, sensors based fire detection systems have been used. The sensor based detection systems contain smoke or/and heat detection sensors. However, the sensors are very sensitive to dust, moisture, and the other chemical pollutant, resulting in malfunction and false alarm. To improve the problems, this paper proposes deep learning algorithm based fire detection systems. Moreover, besides fire and neutral modes, a function of smoke detection is additionally developed because smoke comes first before fires. Therefore, in this research, three classes, i.e., fire, neutral, and smoke. To construct balanced datasets, 900 images for each classes are used. 80 percentage of the dataset is used for train mode and remaining dataset is used for validation mode. To optimize the proposed neural network in train mode, adaptive moment estimation (Adam) is used. In addition, as for hyper parameters in train mode, we used 8192, 128, and 0.0005 as for batch size, epochs, and learning rate. With the trained model, performance evaluation was conducted, we obtained fire f1 score of 0.901, neutral f1-score of 0.821, and smoke f1-score of 0.782, resulting in average f1-score of 0.835. In the area under receiver operating characteristic (ROC) curve (AUC) tests, we achieved fire AUC of 0.98, neutral AUC of 0.95, and smoke AUC of 0.93. |
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