한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

한국컴퓨터정보학회 (Korean Society of Computer Information)
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AI Fire Detection & Notification System

  • Na, You-min (Dept. of Mechanical Engineering, Yonsei University) ;
  • Hyun, Dong-hwan (Dept. of Mechanical Engineering, Yonsei University) ;
  • Park, Do-hyun (Dept. of Mechanical Engineering, Yonsei University) ;
  • Hwang, Se-hyun (Dept. of Mechanical Engineering, Yonsei University) ;
  • Lee, Soo-hong (Dept. of Mechanical Engineering, Yonsei University)
  • 투고 : 2020.11.27
  • 심사 : 2020.12.15
  • 발행 : 2020.12.31
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초록

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.

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.

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참고문헌

  1. Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam, "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications," arXiv preprint arXiv:1704.04861, Apr. 2017.
  2. Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun, "Shufflenet: An extremely efficient convolutional neural network for mobile devices," CVPR 2018, pp. 6848-6856, June 2018, DOI: https://doi.org/10.1109/cvpr.2018.00716
  3. Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C Van Esesn, Abdul A S. Awwal, Vijayan K. Asari, "The history began from alexnet: A comprehensive survey on deep learning approaches," arXiv preprint arXiv:1803.01164, Sep. 2018.
  4. Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le, "Learning transferable architectures for scalable image recognition," CVPR 2018, pp. 8697-8710, June 2018, DOI: https://doi.org/10.1109/cvpr.2018.00907
  5. Xu Qin, Zhilin Wang, "NASNet: A Neuron Attention Stage-byStage Net for Single Image Deraining," arXiv preprint arXiv:1912.03151, May 2019.
  6. Nasnetmobile, https://www.mathworks.com/help/deeplearning/ref/nasnetmobile.html
  7. Mingxing Tan, Quoc V. Le, "Efficientnet: Rethinking model scaling for convolutional neural networks," arXiv preprint arXiv:1905.11946, Sep. 2019.
  8. Paul Viola, Michael J. Jones, "Robust real-time object detection," International journal of computer vision, 4.34-47: 4, Feb. 2001.
  9. YOLO: Real-Time Object Detection, https://pjreddie.com/darknet/yolo/
  10. Mingxing Tan, Ruoming Pang, Quoc V. Le, "Efficientdet: Scalable and efficient object detection," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781-10790, 2020.
  11. Common Objects in Context, https://cocodataset.org/#home
  12. Amazon Simple Notification Service, https://aws.amazon.com/ko/sns/?whats-new-cards.sort-by=item.additionalFields.postDate Time&whats-new-cards.sort-order=desc
  13. Sending a test push notification, https://docs.aws.amazon.com/pinpoint/latest/userguide/messages-mobile.html
  14. Laurence Moroney, "Firebase Cloud Messaging," The Definitive Guide to Firebase. Apress, Berkeley, CA, pp. 163-188, Nov. 2017, DOI: https://doi.org/10.1007/978-1-4842- 2943-9_9
  15. XMPP, https://xmpp.org/
  16. Amazon Simple Email Service, https://aws.amazon.com/ses/?nc1=h_ls
  17. AWS Elemental MediaLive, https://aws.amazon.com/ko/medialive/
  18. Jason Nadon, "Your Content Solution: An Introduction to AWS S3," Website Hosting and Migration with Amazon Web Services. Apress, Berkeley, CA, pp. 15-24, May 2017, DOI: https://doi.org/10.1007/978-1-4842-2589-9_3
  19. Hobin Yoon, Ada Gavrilovska, Karsten Schwan, Jim Donahue, "Interactive use of cloud services: Amazon sqs and s3," 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012). IEEE, pp. 523-530, May 2012, DOI: https://doi.org/10.1109/CC Grid.2012.85
  20. Price, Miles, "Docker : The Comprehensive Beginner's Guide to Take Control of Docker Programming [paper book]," Createspace Independent Publishing Platform, pp. 1-82, Dec. 2017.
  21. Deepak Vohra, "Amazon Fargate Quick Start Guide: Learn how to use AWS Fargate to run containers with ease," Packt Publishing Ltd, pp. 1-169, July 2018.
  22. Josef Spillner, Serhii Dorodko, "Java code analysis and transformation into AWS lambda functions," arXiv preprint arXiv:1702.05510, Feb. 2017.