Early Fire Detection System by Using Automatic Synthetic Dataset Generation Model Based on Digital Twins

被引:3
|
作者
Kim, Hyeon-Cheol [1 ]
Lam, Hoang-Khanh [1 ]
Lee, Suk-Hwan [1 ]
Ok, Soo-Yol [1 ]
机构
[1] Dong A Univ, Dept Comp Engn, Busan 49315, South Korea
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 05期
基金
新加坡国家研究基金会;
关键词
digital twin smart city; particle system; synthetic learning data; early fire detection; object detection;
D O I
10.3390/app14051801
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Fire is amorphous and occurs differently depending on the space, environment, and material of the fire. In particular, the early detection of fires is a very important task in preventing large-scale accidents; however, there are currently almost no learnable early fire datasets for machine learning. This paper proposes an early fire detection system optimized for certain spaces using a digital-twin-based automatic fire learning data generation model for each space. The proposed method first automatically generates realistic particle-simulation-based synthetic fire data on an RGB-D image matched to the view angle of a monitoring camera to build a digital twin environment of the real space. In other words, our method generates synthetic fire data according to various fire situations in each specific space and then performs transfer learning using a state-of-the-art detection model with these datasets and distributes them to AIoT devices in the real space. Synthetic fire data generation optimized for a space can increase the accuracy and reduce the false detection rate of existing fire detection models that are not adaptive to space.
引用
收藏
页数:18
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