• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.685-689
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• 2023

The proposed system in the study aims to detect forest fires in real-time stream data received from the drone-camera. Recently, the number of wildfires has been increasing, and also the large scaled wildfires are frequent more and more. In order to prevent forest fire damage, many experiments using the drone camera and vision analysis are actively conducted, however there were many challenges, such as network speed, pre-processing, and model performance, to detect forest fires from real-time streaming data of the flying drone. Therefore, this study applied image data processing works to capture five good image frames for vision analysis from whole streaming data and then developed the object detection model based on YOLO_v2. As the result, the classification model performance of forest fire images reached upto 93% of accuracy, and the field test for the model verification detected the forest fire with about 70% accuracy.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.9-16
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• 2020

In this paper, we propose a prototype design and implementation of a fire detection algorithm using multiple sensors. The proposed topic detection system determines fire by applying rules based on data from multiple sensors. The fire takes about 3 to 5 minutes, which is the optimal time for fire detection. This means that timely identification of potential fires is important for fire management. However, current fire detection devices are very vulnerable to false alarms because they rely on a single sensor to detect smoke or heat. Recently, with the development of IoT technology, it is possible to integrate multiple sensors into a fire detector. In addition, the fire detector has been developed with a smart technology that can communicate with other objects and perform programmed tasks. The prototype was produced with a success rate of 90% and a false alarm rate of 10% based on 10 actual experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.533-538
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• 2014

This study examines the performance of and searches for improvements to the existing automatic fire detection systems installed at domestic apartment buildings as a basic study for development of intelligent fire detection systems specifically for apartments. Thus, this study aims to find out the problems in performance and maintenance of the existing fire detectors installed at apartment buildings which is the prerequisite process for development of intelligent fire detection system for that specific application. It is also found impossible to check whether or not the detectors installed at each apartment are in an operational state at normal times. This study finds that it is desirable to replace the slow-sensing heat detectors by a smoke and single smoke detectors which can detect a fire at its early stage in an effort to improve the problems of fire detectors installed at apartment buildings presently. Because we need to the independence fire detection system of apartment building.

• Fire Science and Engineering
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• v.34 no.2
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• pp.30-40
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• 2020

In the event of a fire and a disaster, prompt and accurate alarms inside and outside the building are directly related to the minimization of damage and the success of life evacuation. However, due to unwanted fire alarms in automated fire detection systems, the number of dispatches by misunderstanding in the 119 service is increasing. This causes the insensitivity to the safety of building managers and the waste of the fire-fighting power. Therefore, in this study, the statistical databases and literature on unwanted fire alarms in Korea and abroad (USA, UK) were identified and the management systems for unwanted fire alarms were compared and analyzed to identify problems of statistics in the management systems for unwanted fire alarms.

• Fire Science and Engineering
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• v.22 no.4
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• pp.42-49
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• 2008

This research is to provide a scheme for an automatic fire alarm system with higher reliability through solving problems of malfunctioning (false or missing fire alarm) and power interruption (result from frequently unwanted activation, etc) of an automatic fire alarm system. A digital control system with microprocessor-based is proposed to reduce the possibility of malfunctioning through a combinational use of heat, smoke and CO sensors. Higher reliability could be achieved by these multiple sensors based fire detection system and fire distinction algorithm. In this research, we implemented actual fire detection system and conducted fire test to verify improvement on reliability.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.69-71
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• 2022

Today, the AI market is very large due to the development of AI. Among them, the most advanced AI is image detection. Thus, there are many object detection models using YOLOv5.However, most object detection in AI is focused on detecting objects that are stereotyped.In order to recognize such unstructured data, the object may be recognized by learning and filtering the object. Therefore, in this paper, a fire monitoring system using YOLOv5 was designed to detect and analyze unstructured data fires and suggest ways to improve the fire object detection model.

• Journal of the Korean Institute of Gas
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• v.16 no.6
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• pp.107-114
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• 2012

Fire detection system is used for detection and alarm-generation of danger in case of fire. Most fire detection systems being used these days often malfunction from false positive and false negative errors. To improve detection reliability, an integrated fire detection algorithm using multi-senor information of heat, smoke and carbon monoxide detectors is suggested, then built and tested using the LabVIEW environment. Simulated using sensor measurement data offered by National Institute of Standards and Technology (NIST), possibility of reducing false positive and false negative errors is verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2156-2170
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• 2020

This paper presents a fire detection algorithm with a minimal false detection rate, intended for a thermal imaging surveillance environment, whose properties vary depending on temporal conditions of day or night and environmental changes. This algorithm was designed to minimize the false detection alarm rate while ensuring a high detection rate, as required in fire detection applications. It was necessary to reduce false fire detections due to non-flame elements occurring when existing fixed threshold-based fire detection methods were applied. To this end, adaptive flame thresholds that varied depending on the characteristics of input images, as well as the center of gravity of the heat-source and hot-source regions, were analyzed in an attempt to minimize such non-flame elements in the phase of selecting flame candidate blocks. Also, to remove any false detection elements caused by camera shaking, one of the most frequently raised issues at outdoor sites, preliminary decision thresholds were adaptively set to the motion pixel ratio of input images to maximize the accuracy of the preliminary decision. Finally, in addition to the preliminary decision results, the texture correlation and intensity of the flame candidate blocks were averaged for a specific period of time and tested for their conformity with the fire decision conditions before making the final decision. To verify the fire detection performance of the proposed algorithm, a total of ten test videos were subjected to computer simulation. As a result, the fire detection accuracy of the proposed algorithm was determined to be 94.24%, with minimum false detection, demonstrating its improved performance and practicality compared to previous fixed threshold-based algorithms.

• Fire Science and Engineering
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• v.33 no.1
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• pp.85-91
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• 2019

This study evaluated the fire detection performance of an automatic fire extinguishing system for road tunnels, which combines flame wavelength detection technology with flame image detection technology. This fusion technique to improve the fire detection capability can reduce the damage caused by the fire suppression by locating the fire source in the fire and discharging the pressurized water only at the fire source. Experiments were conducted to determine the position of a fire source when a $70cm{\times}70cm$ target was placed at a distance of 15 m, 20 m, 25 m, 30 m, and 35 m, respectively, in a situation where there is a flame and smoke in a tunnel. The performance of the ultraviolet and triple wavelength infrared (IR3) sensors was attenuated due to the interference of thick smoke. In addition when the flame was blocked by thick smoke, the image sensor sensed the smoke and emitted a fire signal.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.298-304
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• 2024

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%.