• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.63-71
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• 2020

In this paper, we propose a fire detection technology using YOLOv3 and EfficientDet, the most reliable artificial intelligence detection algorithm recently, an alert service that simultaneously transmits four kinds of notifications: text, web, app and e-mail, and an AWS system that links fire detection and notification service. There are two types of our highly accurate fire detection algorithms; the fire detection model based on YOLOv3, which operates locally, used more than 2000 fire data and learned through data augmentation, and the EfficientDet, which operates in the cloud, has conducted transfer learning on the pretrained model. Four types of notification services were established using AWS service and FCM service; in the case of the web, app, and mail, notifications were received immediately after notification transmission, and in the case of the text messaging system through the base station, the delay time was fast enough within one second. We proved the accuracy of our fire detection technology through fire detection experiments using the fire video, and we also measured the time of fire detection and notification service to check detecting time and notification time. Our AI fire detection and notification service system in this paper is expected to be more accurate and faster than past fire detection systems, which will greatly help secure golden time in the event of fire accidents.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.04a
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• pp.135-138
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• 2008

Many Victims and property damage are caused in fires. In this paper, an flame detection algorithm is proposed to early alarm fires. The proposed flame detection algorithm is based on 2-stage decision strategy of video processing. The first decision is to check with color distribution of input vidoe. In the second, the candidated region is settled as fire region with activity. As a result of simulation, it is shown that the proposed algorithm is useful for fire recognition.

• Fire Science and Engineering
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• v.34 no.1
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• pp.37-46
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• 2020

This is a basic research on potential application of fire detection by measuring fire detection tendency of indoor air quality measurement factors. In this study, operation experiment using smoke detector sensitivity tester and paper fire experiment specified in UL 268 standards were conducted to evaluate the fire detection tendency of indoor air quality measurement factors. Based on the cross-substitution of values measured in the paper fire experiment, PM10 (excluding average) and HCHO (excluding average and maximum) for the indoor air quality meter (IAQ); PM1.0, PM2.5, and PM10 for IAQ S2; and CO (excluding the average and maximum) for combustion gas analyzers showed consistent tendency despite changes in the measured values for smoke generation under all experimental conditions. In particular, PM10 and CO are considered the most applicable fire detection factors among the factors measured in the experiment.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.768-773
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• 2011

Today's urban transit system is regarded as the important public transportation service which saves passengers' time and provides the safety. Many researches focus on the rapid and protective responses that minimize the losses when dangerous situation occurs. In this paper we proposed the early fire detection and corresponding rapid response method in urban transit system by combining automatic fire detection for video input and the sensor system. The fire detection method consists of two parts, spark detection and smoke detection. At the spark detection, the RGB color of input video is converted into HSV color and the frame difference is obtained in temporal direction. The region with high R values is considered as fire region candidate and stepwise fire detection rule is applied to calculate its size. At the smoke detection stage, we used the smoke sensor network to secure the credibility of spark detection. The proposed system can be implemented at low prices. In the future work, we would improve the detection algorithm and the accuracy of sensor location in the network.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.1
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• pp.31-36
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• 2010

In this paper, an autonomous mobile robot for fire detection is designed. The robot is equipped with thermal sensor which rotates for wide range of detection. The fire detector A2TPMI is used. A data processing method for robust fire detection is also implemented. AD converter and Kalman filter is applied to remove noisy signal from the fire detection sensor.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.19-24
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• 2024

It is not an exaggeration to say that a single fire causes a lot of damage, so fires are one of the disaster situations that must be alerted as soon as possible. Various technologies have been utilized so far because preventing and detecting fires can never be completely accomplished with individual human efforts. Recently, deep learning technology has been developed, and fire detection systems using object detection neural networks are being actively studied. In this paper, we propose a new fire detection system that improves the previously studied fire detection system. We train the YOLOv8 model using refined datasets through improved labeling methods, derive results, and demonstrate the superiority of the proposed system by comparing it with the results of previous studies.

• 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.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.590-598
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• 2017

Recently, there has been an increase in the number of hazardous events, such as fire accidents. Monitoring systems that rely on human resources depend on people; hence, the performance of the system can be degraded when human operators are fatigued or tensed. It is easy to use fire alarm boxes; however, these are frequently activated by external factors such as temperature and humidity. We propose an approach to fire detection using an image processing technique. In this paper, we propose a fire detection method using multichannel information and gray level co-occurrence matrix (GLCM) image features. Multi-channels consist of RGB, YCbCr, and HSV color spaces. The flame color and smoke texture information are used to detect the flames and smoke, respectively. The experimental results show that the proposed method performs better than the previous method in terms of accuracy of fire detection.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.359-367
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• 2015

In this paper, we propose intelligent fire learning and detection system using hybrid visual attention mechanism of human. Proposed fire learning system generates leaned data by learning process of fire and smoke images. The features used as learning feature are selected among many features which are extracted based on bottom-up visual attention mechanism of human, and these features are modified as learned data by calculating average and standard variation of them. Proposed fire detection system uses learned data which is generated in fire learning system and features of input image to detect fire.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.21-27
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• 2015

A conventional fire detection has been developed based on images captured from a fixed camera. However, It is difficult to apply current algorithms to a flying Quad-rotor to detect fire. To solve this problem, we propose that the fire detection algorithm can be modified for Quad-rotor using Ego-motion compensation. The proposed fire detection algorithm consists of color detection, motion detection, and fire determination using a randomness test. Color detection and randomness test are adapted similarly from an existing algorithm. However, Ego-motion compensation is adapted on motion detection for compensating the degree of Quad-rotor's motion using Planar Projective Transformation based on Optical Flow, RANSAC Algorithm, and Homography. By adapting Ego-motion compensation on the motion detection step, it has been proven that the proposed algorithm has been able to detect fires 83% of the time in hovering mode.