Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Early Fire Detection System for Embedded Platforms: Deep Learning Approach to Minimize False Alarms

임베디드 플랫폼을 위한 화재 조기 감지 시스템: 오경보 최소화를 위한 딥러닝 접근 방식

  • Seong-Jun Ro (Department of Information Security Engineering, Sangmyung University) ;
  • Kwangjae Lee (Department of Information Security Engineering, Sangmyung University)
  • 노성준 (상명대학교 정보보안공학과) ;
  • 이광재 (상명대학교 정보보안공학과)
  • Received : 2024.08.21
  • Accepted : 2024.09.02
  • Published : 2024.09.30
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Abstract

In Korea, fires are the second most common type of disaster, causing large-scale damages. The installation of fire detectors is legislated to prevent fires and minimize damage. Conventional fire detectors have limitations in initial suppression of failures because they detect fires when large amounts of smoke and heat are generated. Additionally, frequent malfunctions in fire detectors may cause users to turn them off. To address these issues, recent studies focus on accurately detecting even small-scale fires using multi-sensor and deep-learning technologies. They also aim at quick fire detection and thermal decomposition using gas. However, these studies are not practical because they overlook the heavy computations involved. Therefore, we propose a fast and accurate fire detection system based on multi-sensor and deep-learning technologies. In addition, we propose a computation-reduction method for selecting sensors suitable for detection using the Pearson correlation coefficient. Specifically, we use a moving average to handle outliers and two-stage labeling to reduce false detections during preprocessing. Subsequently, a deep-learning model is selected as LSTM for analyzing the temporal sequence. Then, we analyze the data using a correlation analysis. Consequently, the model using a small data group with low correlation achieves an accuracy of 99.88% and a false detection rate of 0.12%.

Keywords

Acknowledgement

본 연구는 2024학년도 상명대학교 교내연구비를 지원받아 수행하였음(2024-A000-0294).

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