• 조명전기설비학회논문지
• /
• 제25권5호
• /
• pp.39-44
• /
• 2011

Automatic fire alarm system is set up to automatically detect fire on buildings. Because of economic reasons, P-type receiver and a conventional type fire detector is normally used for automatic fire alarm system in Korea. Because early detection of fire is regarded as important, the need of finding technique of fire location increases. This paper is studied a method to improve a reliability and add a function of fire location detection on a conventional type fire detector and P-type receiver. Fire location is detected by a method that controller attached on the receiver and the detector is read with a time lag. A reliability of fire detection alarm system is improved with a method that false fire alarm is able to decrease using two different principle detector together. This paper is studied for basic data of improvement of low-cost addressable automatic fire alarm system.

• 한국컴퓨터정보학회논문지
• /
• 제25권12호
• /
• pp.63-71
• /
• 2020

본 논문에서는 최근 가장 신뢰도 높은 인공지능 탐지 알고리즘인 YOLOv3와 EfficientDet을 이용한 화재 탐지 기술과 문자, 웹, 앱, 이메일 등 4종류의 알림을 동시에 전송하는 알림서비스 그리고 화재 탐지와 알림서비스를 연동하는 AWS 시스템을 제안한다. 우리의 정확도 높은 화재 탐지 알고리즘은 두 종류인데, 로컬에서 작동하는 YOLOv3 기반의 화재탐지 모델은 2000개 이상의 화재 데이터를 이용해 데이터 증강을 통해 학습하였고, 클라우드에서 작동하는 EfficientDet은 사전학습모델(Pretrained Model)에서 추가로 학습(Transfer Learning)을 진행하였다. 4종류의 알림서비스는 AWS 서비스와 FCM 서비스를 이용해 구축하였는데, 웹, 앱, 메일의 경우 알림 전송 직후 알림이 수신되며, 기지국을 거치는 문자시스템의 경우 지연시간이 1초 이내로 충분히 빨랐다. 화재 영상의 화재 탐지 실험을 통해 우리의 화재 탐지 기술의 정확성을 입증하였으며, 화재 탐지 시간과 알림서비스 시간을 측정해 화재 발생 후 알림 전송까지의 시간도 확인해보았다. 본 논문의 AI 화재 탐지 및 알림서비스 시스템은 과거의 화재탐지 시스템들보다 더 정확하고 빨라서 화재사고 시 골든타임 확보에 큰 도움을 줄 것이라고 기대된다.

• 한국화재소방학회:학술대회논문집
• /
• 한국화재소방학회 2008년도 춘계학술논문발표회 논문집
• /
• pp.135-138
• /
• 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.

• 한국화재소방학회논문지
• /
• 제34권1호
• /
• pp.37-46
• /
• 2020

본 논문은 실내공기질 측정인자들의 화재감지 경향성 측정을 통한 화재감지 활용 가능성에 관한 기초 연구이다. 공기질 측정인자들의 화재감지 경향성을 측정하기 위해 연기감지기 감도시험기를 이용한 작동실험과 UL 268에서 규정하고 있는 종이화재실험을 진행하였다. 연기감지기 감도시험기를 이용한 작동실험 및 UL 268 종이화재실험에 측정된 각각의 측정값을 교차 대입한 결과 공기질 측정기(IAQ) S1의 경우 PM 10(평균값제외), HCHO(평균값 및 최대값 제외), IAQ S2의 경우 PM 1.0, PM 2.5, PM 10, 연소가스분석기(CGA)의 경우 CO(평균값 및 최대값 제외)가 모든 실험 조건에서 연기발생에 따른 측정값의 변화를 통해 경향성을 관찰할 수 있었다. 특히, 본 실험 조건에서 측정되는 인자들 중 적응성이 가장 우수한 PM 10 및 CO는 화재감지 인자로 활용 가능할 것으로 생각된다.

• 한국철도학회:학술대회논문집
• /
• 한국철도학회 2011년도 정기총회 및 추계학술대회 논문집
• /
• pp.768-773
• /
• 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.

• 한국전기전자재료학회논문지
• /
• 제37권3호
• /
• pp.261-273
• /
• 2024

This paper is an experimental study on the improvement of smoke and flame detection from different materials with YOLO. For the study, images of fires occurring in various materials were collected through an open dataset, and experiments were conducted by changing the main factors affecting the performance of the fire object detection model, such as the bounding box, polygon, and data augmentation of the collected image open dataset during data preprocessing. To evaluate the model performance, we calculated the values of precision, recall, F1Score, mAP, and FPS for each condition, and compared the performance of each model based on these values. We also analyzed the changes in model performance due to the data preprocessing method to derive the conditions that have the greatest impact on improving the performance of the fire object detection model. The experimental results showed that for the fire object detection model using the YOLOv5s6.0 model, data augmentation that can change the color of the flame, such as saturation, brightness, and exposure, is most effective in improving the performance of the fire object detection model. The real-time fire object detection model developed in this study can be applied to equipment such as existing CCTV, and it is believed that it can contribute to minimizing fire damage by enabling early detection of fires occurring in various materials.

• 한국전자통신학회논문지
• /
• 제5권1호
• /
• pp.31-36
• /
• 2010

본 논문에서는 화재감지를 위한 자율이동로봇을 설계하였다. 로봇은 넓은 범위의 검출을 위해 회전하는 열 센서를 갖추고 있다. 열 감지 센서인 A2TPMI를 사용하였다. 안정적인 데이터를 획득위해 화재감지를 위한 데이터 처리방법으로 AD컨버터와 칼만 필터를 사용하여 하였다.

• 반도체디스플레이기술학회지
• /
• 제23권1호
• /
• pp.19-24
• /
• 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
• /
• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
• /
• pp.1734-1737
• /
• 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
• /
• 제13권3호
• /
• 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.