• International journal of advanced smart convergence
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• v.8 no.2
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• pp.116-125
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• 2019

The fire should extinguish as soon as possible because it causes economic loss and loses precious life. In this study, we propose a new atypical fire and smoke detection algorithm using deep learning and color histogram of fire and smoke. First, input frame images obtain from the ONVIF surveillance camera mounted in factory search motion candidate frame by motion detection algorithm and mean square error (MSE). Second deep learning (Faster R-CNN) is used to extract the fire and smoke candidate area of motion frame. Third, we apply a novel algorithm to detect the fire and smoke using color histogram algorithm with local area motion, similarity, and MSE. In this study, we developed a novel fire and smoke detection algorithm applied the local motion and color histogram method. Experimental results show that the surveillance camera with the proposed algorithm showed good fire and smoke detection results with very few false positives.

• Fire Science and Engineering
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• v.16 no.4
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• pp.28-32
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• 2002

The performance based design in fire detection system, the effect of high airflow and dilution of smoke produced in any fire situation serve to increase the response time of point-type smoke detectors. This study investigated the smoke density of ceiling, under the air stream and in normal status when fire type is smoldering fires. The result of study, smoke generated in the fire was swept away from nearby spot type smoke detector which failed to actuate because dispersed in diluted form around the room. The concept of performance based design in fire detection system of protected area influenced by high airflow provided the need of active fire detection system such as air sampling smoke detection system.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1734-1737
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• 1997

The conventional fire detection devices are operated after a processed fire phase, which are sensing only a high density of somke level or high temperature heat. They are not so precision to detect a fire in the early phase to protect the facility form the fire. We need to develope a new high precision smoke detection system to keep expensive industrial facilities most reliably form fire. A new optical precision smoke detection system was developed. It monitors very low level density of smoke particles in the air. It is operated continously through many years without a stop or any malfunction. The developed precision smoke detection system will be installed in important industrial facilities, such as power plants, underground common tunnel, main control rooms, computer rooms etc.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.2
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• pp.331-336
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• 2017

The need for early fire detection technology is increasing in order to prevent fire disasters. Sensor device detection for heat, smoke and fire is widely used to detect flame and smoke, but this system is limited by the factors of the sensor environment. To solve these problems, many image-based fire detection systems are being developed. In this paper, we implemented a system to detect fire and smoke from camera input images using a convolution neural network. Through the implemented system using the convolution neural network, a feature map is generated for the smoke image and the fire image, and learning for classifying the smoke and fire is performed on the generated feature map. Experimental results on various images show excellent effects for classifying smoke and fire.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.10
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• pp.49-55
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• 2014

This paper is basic study for development of an advanced photoelectric type smoke detector that has high reliability by reducing the occurrence of nuisance alarms. This paper was attempted to distinguish optical characteristics of the typical fire smoke particle and non-fire smoke particle. According to UL 268 standards, three types of test fires (the paper, the wood and the flammable liquid) were used in this paper for measurement of the fire smoke particles, and the water vapor and the cigarette smoke that were known as the main cause of the nuisance alarms were also used for the non-fire smoke particles. A smoke detection chamber was created, which was equipped with one light source and several light sensors for enabling simultaneous detection of light extinction and scattering, respectively. This paper analyzes the optical characteristics of each smoke particle using this chamber.

• Fire Science and Engineering
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• v.22 no.4
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• pp.27-32
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• 2008

In this paper, a flame and smoke detection algorithm is proposed to recognize a fire. Flame and smoke have specific color distribution and continuously change shapes of them. In the proposed flame detection algorithm, specific regions are candidated as flame by color distributions and variations of frames of video. Some of candidated regions are decided as flame by the magnitude of motion vector. To determine smoke in the field of view of camera, edge is important because high frequency component is decreased by it. Candidated region of smoke is assigned by color distributions, inter-frame differences and the value of edge. The candidated region is settled as smoke region with magnitude of motion vector. As results of simulations, it is shown that the proposed algorithm is useful for flame and smoke detection.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.608-623
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• 2023

Purpose: Recently, the number of logistics warehouses has been on the rise. In addition, as the number of such logistics warehouses increases, number of fire accidents also increases every year, increasing the importance of preventing fires in large logistics warehouses. Method: investigated aspirating smoke detectors that are emerging as adaptive fire detectors in logistics warehouses. Then, through fire simulation (FDS), logistics warehouse modeling was conducted to compare and analyze the detection speed of general smoke detectors and aspirating smoke detectors according to four stages of fire growth and three locations of fire in the logistics warehouse. Result: Growth speed in Slow-class fires and Mediumclass fires, the detection speed of aspirating smoke detectors was faster regardless of the location of the fire. However, in Fast-class fires and Ultra-Fast-class fires, it was confirmed that the detection speed of general smoke detectors was faster depending on the location of the fire. Conclusion: It was confirmed that the detection performance of the aspirating smoke detector decreased as the fire growth speed increased and the location of the fire occurred further than the receiver of the aspirating smoke detector. Therefore, even if an aspirating smoke detector is installed in a warehouse that stores combustibles with high fire growth rates, it is judged that an additional smoke detector is attached far away from the receiver of the general smoke detector to increase fire safety.

• Fire Protection Technology
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• s.15
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• pp.28-38
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• 1993

This report is explain the nature of smoke and the principle of smoke detection. The object of this research is to understand the hazard of smoke and select the optimum smoke detectors, according to the types of smoke and the particle size of smoke produced by fire

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.621-632
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• 2013

The most important things for a forest fire detection system are the exact extraction of the smoke from image and being able to clearly distinguish the smoke from those with similar qualities, such as clouds and fog. This research presents an intelligent forest fire detection algorithm via image processing by using the Gaussian Mixture model (GMM), which can be applied to detect smoke at the earliest time possible in a forest. GMMs are usually addressed by making the model adaptive so that its parameters can track changing illuminations and by making the model more complex so that it can represent multimodal backgrounds more accurately for smoke plume segmentation in the forest. Also, in this paper, we suggest a way to classify the smoke plumes via a feature extraction using HSL(Hue, Saturation and Lightness or Luminanace) color space analysis.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.2164.1_2165.1
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• 2009

In this study we suggest fire detection algorithm using fuzzy inference with input variables of temperature and smoke density to detect electrical fire of early stage. The algorithm consists of membership function of temperature and smoke density and fire probability. The antecedent part of the algorithm consists of temperature and smoke density, and the consequent part consists of fire possibility. The inference rules of the algorithm is estimated to input temperature and smoke density obtained by real fire. With the help of algorithms using fuzzy inference we may be diagnose electrical fire precisely.