• Journal of the Korean Society of Marine Environment & Safety
• /
• v.25 no.4
• /
• pp.476-481
• /
• 2019

Early detection of fire is an important measure for minimizing the loss of life and property damage. However, fire and smoke need to be simultaneously detected. In this context, numerous studies have been conducted on image-based fire detection. Conventional fire detection methods are compute-intensive and comprise several algorithms for extracting the flame and smoke characteristics. Hence, deep learning algorithms and convolution neural networks can be alternatively employed for fire detection. In this study, recorded image data of fire in a ship engine room were analyzed. The flame and smoke characteristics were extracted from the outer box, and the YOLO (You Only Look Once) convolutional neural network algorithm was subsequently employed for learning and testing. Experimental results were evaluated with respect to three attributes, namely detection rate, error rate, and accuracy. The respective values of detection rate, error rate, and accuracy are found to be 0.994, 0.011, and 0.998 for the flame, 0.978, 0.021, and 0.978 for the smoke, and the calculation time is found to be 0.009 s.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.05a
• /
• pp.411-414
• /
• 2009

Many victims and property damages are caused in fires every year. In this paper, intelligent fire-detection systems with embedded fire-detection algorithms for early fire detection and alarm is proposed to reduce fire damages by using image processing technique, high speed digital signal processor(DSP) technique, and information technique. The fire detection algorithms used for the proposed systems consist of flame and smoke detection algorithms. If flame or smoke is detected respectively, the corresponding alarm signal can be transferred to management computer. And if flame and smoke is detected simultaneously, the fire alarm signal shall be generated. Through several experiments in the physical environment, it is shown that the proposed system works well without malfunction.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.12
• /
• pp.1596-1603
• /
• 2018

Rapidly detecting and warning of fires is necessary for minimizing human injury and property damage. Generally, when fires occur, both the smoke and the flames are generated, so fire detection systems need to detect both the smoke and the flames. However, most fire detection systems only detect flames or smoke and have the disadvantage of slower processing speed due to additional preprocessing task. In this paper, we implemented a fire detection system which predicts the flames and the smoke at the same time by constructing a CNN model that supports multi-labeled classification. Also, the system can monitor the fire status in real time by using Grad-CAM which visualizes the position of classes based on the characteristics of CNN. Also, we tested our proposed system with 13 fire videos and got an average accuracy of 98.73% and 95.77% respectively for the flames and the smoke.

• IEIE Transactions on Smart Processing and Computing
• /
• v.6 no.4
• /
• pp.237-245
• /
• 2017

This paper presents a fire detection algorithm using color and motion models from video sequences. The proposed method detects change in color and motion of overall regions for detecting fire, and thus, it can be implemented in both fixed and pan/tilt/zoom (PTZ) cameras. The proposed algorithm consists of three parts. The first part exploits color models of flames and smoke. The candidate regions in the video frames are extracted with the hue-saturation-value (HSV) color model. The second part models the motion information of flames and smoke. Optical flow in the fire candidate region is estimated, and the spatial-temporal distribution of optical flow vectors is analyzed. The final part accumulates the probability of fire in successive video frames, which reduces false-positive errors when fire-like color objects appear. Experimental results from 100 fire videos are shown, where various types of smoke and flames appear in indoor and outdoor environments. According to the experiments and the comparison, the proposed fire detection algorithm works well in various situations, and outperforms the conventional algorithms.

• Fire Science and Engineering
• /
• v.20 no.2 s.62
• /
• pp.8-13
• /
• 2006

Numerical simulations were carried out for a fire in a clean room to confirm needs of a smoke control system and a sprinkler system, and to investigate a possible smoke spread-out. For a 1 MW methanol fire in a space of $39m{\times}13m$ floor and 4 m high, smoke spread-out was scrutinized for failure of the sprinkler system and/or the smoke control system. It was shown that the smoke control system removes smoke safely without the sprinkler system and that the sprinkler system is required to suppress smoke generation and spread of the fire, and to remove the smoke quickly. It was also confirmed that highly reliable sprinkler heads and automatic fire detection system are required for the sprinkler and smoke control systems.

• Proceedings of the IEEK Conference
• /
• 2007.07a
• /
• pp.219-220
• /
• 2007

In this paper, we proposed image processing technique for automatic real time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurred in the tunnel, it is necessary to have a system to minimize and to discover the incident as fast as possible. The fire and smoke detection is different from the forest fire detection as there are elements such as car and tunnel lights and others that are different from the forest environment so that an indigenous algorithm has to be developed. The two algorithms proposed in this paper, are able to detect the exact position, at the earlier stage of incident.

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

• Fire Science and Engineering
• /
• v.18 no.4
• /
• pp.42-51
• /
• 2004

This paper presents a study on the analysis of fire detection system using fuzzy logic with input variables of temperature and smoke density. The input variables for the fuzzy logic algorithm are measured by fire experiment of small scale with temperature detector and smoke detector. The antecedent part of fuzzy rules consists of temperature and smoke density, and the consequent part consists of fire possibility. Also the triangular fuzzy membership function is chosen for input variables and fuzzy rules to simplify computation. In order to calculate fuzzy values of such fuzzy system, a computer program is developed with Matlab based on graphics user interface. The experiment was conducted with paper and ethanol to simulate flaming fire and with plastic and sawdust to model smoldering fire. The results showed that the fire detection system presented here was able to diagnose fire very precisely. With the help of algorithms using fuzzy logic we could distinguish whether fire or not.

• Fire Science and Engineering
• /
• v.26 no.6
• /
• pp.51-56
• /
• 2012

The onset of fire on the International Space Station (ISS) is a critical problem that can threaten the life of crew members onboard and thus instantaneous fire detection and extinguishment technology has been considered as one of the most important aspects in the ISS operation. In the present study, a numerical analysis was performed to better understanding of the characteristics of smoke behaviors and detection in a pressurized module of the ISS using the NIST Fire Dynamic Simulator (FDS). Numerical results indicate that the smoke flow patterns under zero-gravity condition are clearly different from those under normal gravity condition. In addition, the results obtained from numerical simulations coupled with the PM internal flows are expected to provide basic and useful information in designing the microgravity fire detection devices and establishing in fire response protocol for astronauts or the crew members.

• Journal of Korean Association for Spatial Structures
• /
• v.10 no.2
• /
• pp.135-145
• /
• 2010

The objective of this study is to draw basic data for the application of the fire protection planning for the future plan large enclosure buildings in Korea through an analysis of its characteristics by case studies of the domestic and foreign large scale gymnasiums. In this study, domestic building codes for the fire protection are investigated and fire detection systems, fire extinguishing systems, smoke control systems and evacuation systems of three large scale gymnasiums located at Korea and eight foreign countries are compared and analyzed. The results of this study show that infrared light fire detection system and flame detector for spacial characteristics are potentially used in fire protection systems of large scale gymnasiums: dry type sprinkler and sprinkler water gun are adopted in fire detection system; and smoke accumulation system is widely utilized in smoke control system.