인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 저널정보
- 한국정보통신학회 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING 2017 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING Vo.9 No.1
- 발행연도
- 2017.6
- 수록면
- 261 - 264 (4page)
이용수
초록· 키워드
Various fire detection systems have been constructed to prevent disastrous fire. However, existing fire detection systems are limited to practical applications due to lower detection accuracy and frequent alerts caused by incorrect operations. Previous fire detection systems have only focused on detecting flames. Therefore they can mistake the flames of candles or gas ranges as a fire. They also cannot provide additional life-saving information, such as the location of people or fire extinguishers. Thus, we have tried to construct a new fire detection system which can improve flame detection accuracy, does not incorrectly identify the flame of candles or gas ranges as a fire, and also provide additional lifesaving information.
Faster R-CNN is a deep learning algorithm that detects classes and locations of objects, as well as fires, in real-time by using CNN. We have built our fire detection system based on Faster R-CNN. In order to evaluate the performance of our fire detection system, we used various images such as forest fires, gas range fires, and candle flames. Consequently, the fire detection rate of our system was very good at 99.24%. In addition, we analyzed its object detection performance involving 14 classes, such as people, fire extinguishers, doors, pets, etc. Finally, the mAP (mean Average Precision) was relatively high at 0.7863.
상세정보 수정요청해당 페이지 내 제목·저자·목차·페이지Faster R-CNN is a deep learning algorithm that detects classes and locations of objects, as well as fires, in real-time by using CNN. We have built our fire detection system based on Faster R-CNN. In order to evaluate the performance of our fire detection system, we used various images such as forest fires, gas range fires, and candle flames. Consequently, the fire detection rate of our system was very good at 99.24%. In addition, we analyzed its object detection performance involving 14 classes, such as people, fire extinguishers, doors, pets, etc. Finally, the mAP (mean Average Precision) was relatively high at 0.7863.
정보가 잘못된 경우 알려주세요!
목차
- Abstract
- I. INTRODUCTION
- II. IMPLEMENTATION OF A FIRE DETECTION USING FASTER R-CNN
- III. TEST AND ANALYSIS
- IV. DISCUSSION AND CONCLUSIONS
- REFERENCES
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