• Journal of the Korean Society of Hazard Mitigation
• /
• v.10 no.5
• /
• pp.49-54
• /
• 2010

The wooden cultural assets have the characteristics such as the fast spread of flame and leading to total destruction. Therefore, there is a need for a system for early countermeasure of recognized problem, along with the technological response for accurately recognizing the situation, for the prevention and early suppression of fire. To utilize such technology for detecting the situation through the latest ubiquitous technology and for a quick response to suppress fire, the ubiquitous sensor network (USN) technology, flame detector, image sensor, USN-based cultural asset disaster prevention management application case and malfunction identification system realization were examined in this study and the study result was presented focusing on the flame detector malfunction identification system for the ubiquitous-type cultural asset disaster prevention system.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.9 no.1
• /
• pp.9-18
• /
• 2010

Our country has so large mountain area that the tunnel construction is inevitable and the need of incident detection that provides safe management of tunnels is increasing. In this paper, we suggest a tunnel incident detection system using computer vision techniques, which can detect the incidents in a tunnel and provides the information to the tunnel administrative office in order to help safe tunnel operation. The suggested system enhances the processing speed by using simple processing algorithm such as image subtraction, and ensures the accuracy of the system by focused on the incident detection itself rather than its classification. The system is also cost effective because the video data from 4 cameras can be simultaneously analyzed in a single PC-based system. Our system can be easily extended because the PC-based analyzer can be increased according to the number of cameras in a tunnel. Also our web-based structure is useful to connect the other remotely located tunnel incident systems to obtain interoperability between tunnels. Through the experiments the system has successfully detected the incidents in real time including dropped luggage, stoped car, traffic congestion, man walker or bicycle, smoke or fire, reverse driving, etc.

• Korean Journal of Remote Sensing
• /
• v.36 no.2_1
• /
• pp.179-197
• /
• 2020

Topographic normalization reduces the terrain effects on reflectance by adjusting the brightness values of the image pixels to be equal if the pixels cover the same land-cover. Topographic effects are induced by the imaging conditions and tend to be large in high mountainousregions. Therefore, image analysis on mountainous terrain such as estimation of wildfire damage assessment requires appropriate topographic normalization techniques to yield accurate image processing results. However, most of the previous studies focused on the evaluation of topographic normalization on satellite images with moderate-low spatial resolution. Thus, the alleviation of topographic effects on multi-temporal high-resolution images was not dealt enough. In this study, the evaluation of terrain normalization was performed for each band to select the optimal technical combinations for rapid and accurate wildfire damage assessment using PlanetScope images. PlanetScope has considerable potential in the disaster management field as it satisfies the rapid image acquisition by providing the 3 m resolution daily image with global coverage. For comparison of topographic normalization techniques, seven widely used methods were employed on both pre-fire and post-fire images. The analysis on bi-temporal images suggests the optimal combination of techniques which can be applied on images with different land-cover composition. Then, the vegetation index was calculated from the images after the topographic normalization with the proposed method. The wildfire damage detection results were obtained by thresholding the index and showed improvementsin detection accuracy for both object-based and pixel-based image analysis. In addition, the burn severity map was constructed to verify the effects oftopographic correction on a continuous distribution of brightness values.

• Journal of Satellite, Information and Communications
• /
• v.8 no.4
• /
• pp.89-94
• /
• 2013

This paper presents color video flame detection algorithm based on wavelet transform to remove detection of flickering non-flame objects. Conventional flame detection algorithms consist of simple or mixed functions using colors, temporal and spatial characteristics. But those algorithms detect non-flame objects as flame regions sometimes. False alarm reasons are flame-like objects with regular flickering lights such as car signal lamps, alarm lights etc. The proposed algorithm is to reduce false detection which is occurred in periodic flickering lights. At first, It segments the candidate flame regions by using frame difference, flame colors. Then it distinguish flame regions and non flame regions including flickering car lights by analyzing wavelet coefficients. Computer simulation results showed that the proposed algorithm removes false detection due to the periodic flickering lamps by performing 97.9% of correct detection rate while false detection rate is 7.3%.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.20 no.6
• /
• pp.1161-1175
• /
• 2018

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for 'person' object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.1
• /
• pp.22-28
• /
• 2021

This is a basic study on the development of deep learning-based algorithms to detect smoke before the smoke detector operates in the event of a ship fire, analyze and utilize the detected data, and support fire suppression and evacuation activities by predicting the spread of smoke before it spreads to remote areas. Proposed algorithms were reviewed in accordance with the following procedures. As a first step, smoke images obtained through fire simulation were applied to the YOLO (You Only Look Once) model, which is a deep learning-based object detection algorithm. The mean average precision (mAP) of the trained YOLO model was measured to be 98.71%, and smoke was detected at a processing speed of 9 frames per second (FPS). The second step was to estimate the spread of smoke using the coordinates of the boundary box, from which was utilized to extract the smoke geometry from YOLO. This smoke geometry was then applied to the time series prediction algorithm, long short-term memory (LSTM). As a result, smoke spread data obtained from the coordinates of the boundary box between the estimated fire occurrence and 30 s were entered into the LSTM learning model to predict smoke spread data from 31 s to 90 s in the smoke image of a fast fire obtained from fire simulation. The average square root error between the estimated spread of smoke and its predicted value was 2.74.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.9
• /
• pp.4472-4477
• /
• 2013

This paper describes the color features of smoke in each standard color model in order to present the most suitable color model for somke detection in video surveillance system. Histogram intersection technique is used to analyze the difference characteristics between color of smoke and color of non smoke. The considered standard color models are RGB, YCbCr, CIE-Lab, HSV, and if the calculated histogram intersection value is large for the considered color model, then the smoke spilt characteristics are not good in that color model. If the calculated histogram intersection value is small, then the smoke spilt characteristics are good in that color model. The analyzed result shows that the RGB and HSV color models are the most suitable for color model based smoke detection by performing respectively 0.14 and 0.156 for histogram intersection value.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.463-466
• /
• 2010

In this paper, CCTV based fire surveillance system should aim to development. Advantages and Disadvantages analyzed of Existing sensor-based fire surveillance system and video-based fire surveillance system. To national support U-City, U-Home, U-Campus, etc, spread the ubiquitous environment appropriate to fire surveillance system model and a fire judgement technology. For this study, Microsoft LifeCam VX-1000 using through the capturing images and analyzed for apple and tomato, Finally we used H.264. The client uses the Linux OS with ARM9 S3C2440 board was manufactured, the client's role is passed to the server to processed capturing image. Client and the server is basically a 1:1 video communications. So to multiple receive to video multicast support will be a specification. Is fire surveillance system designed for multiple video communication. Video data from the RGB format to YUV format and transfer and fire detection for Y value. Y value is know movement data. The red color of the fire is determined to detect and calculate the value of Y at the fire continues to detect the movement of flame.

• Journal of Satellite, Information and Communications
• /
• v.9 no.2
• /
• pp.33-37
• /
• 2014

This paper propose a method to sort flame regions and non-flame regions in a color image based on temperature Characteristics of flame. The traditional algorithms simply detect flame regions those are colored between yellow and red and there are lot of false detection in this method. But the colors of real flame are fallen between white and red and flame color variation over the flame. In this paper, it reduce false detection by separating colors according to temperature Characteristics of flame. The proposed method firstly finds a color model to express the temperature Characteristics of fire and then the color model is non-linearly quantized based on color values and analyzed using histogram and finally detect the candidate flame regions. The proposed method has 71.8% of matching rate and if it is compared with non-matching rate of traditional algorithms, the non-matching rate is improved by 27 times than others.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.4
• /
• pp.452-459
• /
• 2018

Conventional fire fighting robots are controlled by a remote control to monitor the fire scene or to suppress the fire. However, this method has a problem that it takes a long time to prepare robot and input it to fire place in the golden time after the fire, so that it can not sufficiently serve as a fire fighting robot. Using the autonomous driving fire monitoring robot, when a fire signal is generated, in conjunction with a fire receiver a moving robot takes a video of the fire scene and delivers the image to the fire department, so that the fire fighter can decide if it is real fire or not. Thereby it is possible to prevent a sudden spread of an accident by providing a quick judgment opportunity and at the same time suppressing the fire early. In this paper, we propose an architecture of the autonomous mobile fire monitoring robot and the communication protocol required for the robot to work with the fire receiver. A communication protocol is designed to control multiple fire monitoring robots in real time, and a communication with a fire receiver is designed as a hierarchical network to serve as an interface of an Ethernet network using wireless Wi-Fi. The fire monitoring robot and the wireless communication of the fire receiving period are implemented and the effectiveness of the operation is confirmed through the field test.